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CONTROLLED MOBILITY FOR PERFORMANCE
ENHANCEMENTS IN WIRELESS SENSOR NETWORKS

By

J ayanthi Rao

A DISSERTATION
Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of
DOCTOR OF PHILOSOPHY

Electrical Engineering

2009

ABSTRACT

CONTROLLED MOBILITY FOR PERFORMANCE ENHANCEMENTS IN
WIRELESS SENSOR NETWORKS

By

J ayanthi Rao

The objective of this thesis is to investigate the role of controlled node mobility for
enhancing data collection and energy performance in wireless sensor networks. The goal
is to develop joint mobility and packet routing protocols, and to study the associated
tradeoffs from a network design standpoint. An understanding of these tradeofi‘s help in
formulating design guidelines that can be applied to network deployments. Specific
contributions of the thesis are as follows.

First, for networks with mobile sensors and static base stations, a new paradigm for
extending network life has been explored by introducing energy-aware node mobility
along with an adaptive packet routing protocol. A fully distributed and localized mobility
control and packet routing framework for collective extension of network lifetime was
developed. Via analytical modeling and simulation experiments it was established that
using cooperative and energy-aware node mobility, it is feasible to significantly extend a
sensor network’s operating life even in situations where the energy cost of physical node
movement is modeled as high as up to four orders of magnitude larger than the energy
cost for packet transmissions. The investigated paradigm in this part of the thesis applies
to networks with mobile sensors such as terrestrial and aquatic robotic micro-vehicles
with sensing abilities.

The second part of the thesis deals with networks with static sensors and mobile sinks

which correspond to applications such as Unmanned Air/Ground Vehicles (UAV, UGV)
based data collection from unattended static sensor units. In this part of the thesis, a
generalized analytical formulation for mobile sensor based data collection has been
developed with varying levels of static sensor to mobile sink multi-hop routing, which is
parameterized as a hop-bound factor k. The key concept is to appropriately adjust the
extent of multi-hop routing and the corresponding sink trajectory by varying the hop
bound factor k. The main result is that for given sensor data rates and node density, there
are clear bounds to the extent of multi-hop routing that can be sustained in a network. In
other words, to maintain data collection stability from delay and capacity standpoints, the
extent of multi-hop routing (hop-bound factor k) needs to be within a feasible range —
rendering data collection with static sink often infeasible.

This result was experimentally validated by developing a Network-Assisted Data
Collection (NADC), which is a distributed and location-unaware sink navigation and data
routing protocol. Finally, the NADC framework was extended for networks with spatially
heterogeneous data generation rates, and the routing feasibility results were validated for

such scenarios.

Copynghtby
Jayanthi Rao

2009

To My Family
For All Their

Love and Support

Acknowledgements

I owe my deepest gratitude to my advisor Dr. Subir Biswas for guiding and supporting
me throughout the course of my doctoral study. His ideas, expertise and sound advice
have been a major influence on this thesis. I have learned a great deal under his able
mentorship during this research work. I would also like to thank the other members of my
committee namely Dr. Nihar Mahapatra, Dr. Erik Goodman and Dr. Betty Cheng for their
helpful suggestions, support and advice.

I am greatly indebted to Dean Udpa, Dr. Reinhard, Dr. Shanblatt, Dr. Barbara O’Kelly,
ECE department and the graduate school for their support and encouragement in my
endeavor. Also, this research study required long and extensive simulations and would
not have been possible without the availability of HPCC computing resources.

Very special thanks go to my colleagues and fi'iends in the NeEWS laboratory -
Muhannad Quwaider, Anthony Plurnmer, Mahmoud Taghizade, Tao Wu and Fan Yu with
whom I have worked closely during the last few years and enjoyed sharing research ideas
and accomplishments.

Lastly, and most importantly, I thank my family for their understanding, motivation and

support throughout this research work.

vi

TABLE OF CONTENTS

 

 

 

 

LIST OF TABLES - - - - - xii
LIST OF FIGURES -- - - xiii
LIST OF ABBREVIATIONS - - ....... - xix
CHAPTER 1. INTRODUCTION - 1
1.1 WIRELESS SENSOR NETWORKS ............................................................................... 1
1.2 APPLICATIONS OF WIRELESS SENSOR NETWORKS .................................................. 5
1.2.1 Continuous Monitoring ...................................................................................... 6
1.2.2 Event Monitoring ............................................................................................... 8

1.3 PERFORMANCE METRICS ........................................................................................ 8
1.4 ENERGY IN SENSOR NETWORKS ........................................................................... 10
1.4.1 Sources of Energy Expenditure ........................................................................ 10
1.4.2 Energy Conservation Mechanisms ................................................................... 11

CHAPTER 2. CONTROLLED MOBILITY AND NETWORK PERFORMANCE 13

 

2.1 INTRODUCTION ..................................................................................................... I 3
2.2 PROBLEM DEFINITION: LIFETIME LIMITATION IN STATIC SENSOR NETWORKS ...... 14
2.3 CONTROLLED MOBILITY FOR DATA COLLECTION ................................................. 16
2.4 RELATED WORK ................................................................................................... I7
2. 4. 1 Mobile Sensors and Static Sink ........................................................................ 18
2.4.2 Static Sensors and Mobile Sink ........................................................................ 18
2.4.2.1 Real-time ................................................................................................... 19
2.4.2.2 Non-real-time ............................................................................................ 23
2.4.3 Static Sensors, Static Sink and Mobile Relays ................................................. 25
CHAPTER 3. THESIS SCOPE AND CONTRIBUTIONS 27
3.1 COLLABORATIVE NODE MOBILITY FOR ENERGY CONSERVATION ......................... 28
3.2 GENERALIZED FRAMEWORK FOR MOBILE SINK TRAJECTORY PLANNING AND
ROUTING ......................................................................................................................... 29
3.2.1 Generalized Problem Formulation .................................................................. 29
3.2.2 Distributed Algorithms/Protocols for Generalized Framework ...................... 32

vii

CHAPTER 4. COLLABORATIVE NODE MOBILITY FOR ENERGY

 

CONSERVATION 34
4.1 INTRODUCTION AND RELATED WORK ................................................................... 34
4.2 BIOLOGICALLY INSPIRED PROBLEM FORMULATION .............................................. 35
4.3 CONTROLLED MOBILITY FOR ENERGY LOAD DISTRIBUTION ................................ 37

4.3.1 Example Scenario ............................................................................................ 3 7
4.4 NETWORK AND APPLICATION MODEL ................................................................... 39
4.5 PROBLEM FORMULATION ...................................................................................... 40
4.6 ENERGY AWARE SWAP PROTOCOL (EASP) ........................................................... 40

4. 6. I EASP Architecture and Protocol Outline ......................................................... 40

4.6.1.1 Neighbor Discovery .................................................................................. 41
4.6.1.2 Swap Evaluation and Partner Selection .................................................... 41
4.6.1.3 Swap Initiation and Conflict Resolution ................................................... 42
4.6.1.4 Physical Swap with Route Handoff .......................................................... 42

4.6.2 Interaction with the Core Protocols ................................................................. 43
4.7 PROTOCOL DETAILS .............................................................................................. 43

4. 7. 1 Swap Evaluation and Partner Selection .......................................................... 43

4. 7.2 Swap Initiation and Conflict Resolution .......................................................... 48

4. 7.3 Physical Swap with Handofl ............................................................................ 50

4.7.3.1 State Machine for the Physical Swap ........................................................ 51
4.8 DESIGN GENERALIZATION FOR ARBITRARY TOPOLOGIES ..................................... 53
4.9 ANALYSIS OF SWAPS IN LINEAR NETWORKS ......................................................... 54

4.9.1 Performance Upper Bound .............................................................................. 54

4.10 SUMMARY ............................................................................................................. 58

CHAPTER 5. ENERGY AWARE SWAPPING PROTOCOL - PERFORMANCE

 

EVALUATION - 59
5.1 RESULTS FROM C BASED SIMULATOR ................................................................... 60
5.1.1 Linear Topology ............................................................................................... 62
5.1.2 Two-dimensional Topology ............................................................................... 66

5.2 DETAILED PACKET LEVEL NS2 SIMULATION ........................................................ 69

5. 2. 1 Evaluated Protocols ......................................................................................... 70
5.2.2 Performance Results ........................................................................................ 71
5.2.2.1 Benefits of Swap in Linear Topologies ..................................................... 71
5.2.2.2 Swap in Two-dimensional Topologies ...................................................... 73

5.3 SUMMARY AND CONCLUSIONS .............................................................................. 87

CHAPTER 6. GENERALIZED FRAMEWORK FOR JOINT TRAJECTORY AND

 

ROUTING OPTIMIZATION - -- - 88
6.1 SENSOR NETWORK DATA COLLECTION ................................................................. 88
6.2 APPLICATION MODEL AND PROBLEM STATEMENT ................................................ 90
6.3 DATA COLLECTION MODES ................................................................................... 94
6.4 PARAMETERIZATION OF MULTI-HOP ROUTING ...................................................... 94

viii

6. 5 PROBLEM FORMULATION ...................................................................................... 96

6.6 MODEL FOR THE HOP BOUND FACTOR K ............................................................... 98
6.6.1 Trajectory Length ........................................................................................... 100
6.6.2 Trip Collection-Time ...................................................................................... 101

6.6.2.1 Numerical Evaluation of Trip Collection-time ....................................... 103

6.6.3 Energy Consumption ...................................................................................... 105
6.6.3.1 Impacts of Data Collection Modes on Energy ........................................ 110

6.7 APPLICABILITY OF MODEL TO NON-CIRCULAR COVERING TRAJECTORIES .......... I 10
6.8 SUMMARYAND CONCLUSIONS ............................................................................ 112
CHAPTER 7. NETWORK-ASSISTED DATA COLLECTION (NADC) ................ 113

7.1 CONCEPT SUMMARY ........................................................................................... 113

7.2 NADC: ALGORITHMIC DETAILS ......................................................................... 116
7. 2. 1 Construction of Overlay Navigation Topology .............................................. 116
7.2.2 Distributed Trajectory Computation .............................................................. 119
7.2.3 Run-time Navigation (RN) ............................................................................. 120
7.2.4 Designated Gateway (DG) Identification and Routing .................................. 124

7.3 SUB/MARY ........................................................................................................... 125

CHAPTER 8. NETWORK ASSISTED DATA COLLECTION: PERFORMANCE

 

EVALUATION - _ - 126
8.1 SIMULATION SETUP ............................................................................................ 126
8.1.1 Implemented Protocols ................................................................................... 126

8.2 SINGLE HOP DATA COLLECTION (K=l) ............................................................... 128
8.3 MULTI-HOP DATA COLLECTION (K > 1) ............................................................... 134
8.3.1 Impact of Network Density ............................................................................. 1 3 9

8.4 PERFORMANCE SUMMARY .................................................................................. 141
8.5 SUMMARY AND CONCLUSIONS ............................................................................ 141

CHAPTER 9. STABILITY ANALYSIS OF MULTI—HOP DATA COLLECTION. 143

9.1. MOTIVATION ..... . .................................................................................................. 143
9.2 ROUTING THRESHOLDS ....................................................................................... 143
9. 2.] Stop threshold ................................................................................................. 143
9.2.2 Stability threshold ........................................................................................... 145
9.2.2.1 Bound for Data Generation Rate ............................................................. 146

9.3 NUMERICAL EVALUATION OF THE COLLECTION TIME ......................................... 147
9.4 PROPERTIES OF THE STOPAND STABILITY THRESHOLDS ..................................... 152
9. 4.] Applicability to Non-circular Terrains ........................................................... 155
9.5 SIMULATION EXPERIMENTAL RESULTS .................................................. 157
9.5.1 Impacts of Data Rate ...................................................................................... 15 7
9.5.2 Thresholds for Hop-bound (k) ........................................................................ 159
9.5.3 Guidelines for Choosing Hop-bound Factor k .............................................. 164

ix

9-6 SUMMARY AND CONCLUSIONS ............................................................................ 165

 

CHAPTER 10. CAPACITY-AWARE STABILITY ANALYSIS 168
1 0.1 MOTIVATION ....................................................................................................... 168
10.1.1 Feasibility Range for Stable Operation ...................................................... 168
1 0.2 THEORETICAL ANALYSIS OF FEASIBILITY RANGE ............................................... 169
10.2.1 Modifications to the Model ......................................................................... 169
10.2.2 Stable Operating Range .............................................................................. 1 71
10.3 IMPACT ON STOP THRESHOLD ............................................................................. 172
I 0.4 IMPACT OF CAPACITY-AWARENESS: NUMERICAL EVALUATION ............................ 172
10.4.1 Trip Collection-time .................................................................................... 1 73
10.4.2 Stop and Stability Thresholds ..................................................................... 1 78
10.4.3 Discussion ................................................................................................... 181
10.5 PERFORMANCE EVALUATION VIA SIMULATION EXPERIMENTS ............................ 182
10.5.1 Impact of Data Rate on Performance ......................................................... 183
10.5.2 Impact of Network Parameters on the Feasibility Range and Stop Threshold
186
10.6 SUMMARY AND CONCLUSIONS ............................................................................ 189

C lIAPT ER 11. RATE AWARE SINK TRAJECTORY DESIGN FOR

 

HETEROGENEOUS NETWORKS 192
11.1 MOTIVATION ....................................................................................................... 192
11.2 RELATED WORK ................................................................................................. 193
11.3 ANALYSIS OF REGION-SPECIFIC MULTI-HOP ROUTING ........................................ 194

11.3.1 Unstable Operation “call > 2') .................................................................. 197
11.3.2 Stable Operation (teal! > r ) with High Energy Expenditure ..................... 198
11.3.3 Stable Operation (tcoll > r) with High PacketAccumulation and Drops. 199

l 1.4 PROBLEM FORMULATION .................................................................................... 200

l l .5 2 -AWARE TRAJECTORY DETERMINATION (LATD) ............................................ 200
11.5.1 Protocol Details .......................................................................................... 20]
11.5.1.1 Data Forwarding Tree ............................................................................. 201

11.5.1.2 Navigation Agent Selection .................................................................... 202

11.5.2 ProtocolBehaviorAnalysis......................................................................'..206

1 1 .6 SIMULATION PERFORMANCE EVALUATION .......................................................... 207
11.6.1 Performance Comparison of LA T D with Regional k Selection .................. 208
11.6.2 Performance Comparison of LAT D with Unconstrained NA Selection ..... 210
11.6.2.1 Two-region Network Scenarios ............................................................... 210

11.6.2.2 Four-region Network Scenarios .............................................................. 216

1 l -7 SUMMARYAND CONCLUSIONS ............................................................................ 217
CHAPTER 12. THESIS CONTRIBUTIONS AND FUTURE WORK ................... 219
12. 1 CONTRIBUTIONS ................................................................................................. 219

l 2 .2 FUTURE WORK ................................................................................................... 221

APPENDIX A: NUMERICAL ANALYSIS OF FEASIBILITY RANGE FOR

 

FULL COVERAGE SCENARIO 223
A.l TRAJECTORY LENGTH (L) ....................................................................................... 223

A.2 UPDATE TIME (Tu ) ................................................................................................ 223

A3 NUMERICAL EVALUATION ....................................................................................... 224

AA CONCLUSIONS ........................................................................................................ 228
AIPENDIX B: LENGTH OF SAMPLE COVERING TRAJECTORIES ............ 230
REFERENCES _ 231

 

xi

List of Tables

Table 1. Sensor node and vehicle motor specifications ..................................................... 60
Table 2: Energy per packet (Joule) .................................................................................... 63
Table 3: Energy per packet (in milliJoules) ....................................................................... 79
Table 4: Symbols definition .............................................................................................. 97

xii

List of Figures

Figure 1-1: NIMS deployment in James San J acinto Mountains Reserve [25] ................... 6
Figure 1-2: NAMOS at Lake Fulmor and a robotic boat [28] ............................................. 7
Pi gure 2-1: a) Sensor network with multi-hop data collection b) Energy drainage profile
---- ....................................................................................................................................... 16
F i gure 2-2: Design options for data collection in sensor networks ................................... 17
F i gure 2-3: Solution classification for mobile sink data collection ................................... 20
F i gure 3-1: Pictorial View of the scope of the thesis ......................................................... 27
F i gure 3-2: Solution mapping to latency and lifetime performance .................................. 3O
Fi gure 4-1: Benefits of location swap for extending network longevity ........................... 38
Fi gure 4-2: EASP protocol modules .................................................................................. 41
Fi gure 4-3: Pseudo code for swap evaluation and partner selection ................................. 47
Fi gure 4-4: Timing diagram for swap initiation and conflict resolution ........................... 48
Figure 4-5: Straight and diagonal handoffs ....................................................................... 49
Figure 4-6: State machine for the swap operation at Swapper .......................................... 52
Figure 4-7: State machine for the swap Operation at Swappee .......................................... 53
Figure 4-8: A linear network with n nodes and one base station ...................................... 54
Figure 4-9 Swap sequence for maximum network life in a 3 node linear network .......... 56
Figure 4-10: Trajectory of N2 and N3 during the maximum life swap sequence ............. 57
Figure 5-1: Network life versus network size ................................................................. 62
Figure 5-2: Spread of nodal energy with time ................................................................... 63
Figure 5-3: Impact of swap cost on network life ............................................................... 64
Figure 5-4: Average remaining energy at the end of network life .................................... 65
Figure 5-5: Network life versus network size ................................................................... 66
Figure 5-6: Remaining alive nodes with time ................................................................... 68

xiii

Figure 5-7: Lifetime for different protocols in linear topology ......................................... 72

Figure 5-8: Network lifetime of 2-dimensional networks ................................................. 74
Figure 5-9: Residual energy variance in a 8x8 network .................................................... 76
Figure 5-10: Average remaining energy at the end of network life .................................. 77
Figure 5-11: Per node swap count for EASP protocols ..................................................... 79
Figure 5-12: a) End-to-end delay and b) route length ....................................................... 81
F i gure 5-13: a) Control overhead, and b) Packet delivery ratio ........................................ 81

F i gure 5-14: Impact of swap threshold on EASP performance ......................................... 83
F i gure 5-15: Impact of data packet size on performance .................................................. 84
Fi gure 5-16: Impact of swap cost to communication cost ratio ........................................ 85
F i gure 5-17: Error Sensitivity a) Lifetime, and b) Delivery ratio ..................................... 86

Fi gure 6-1: Different routing scenarios in static and mobile-sink-based data collection..89

Fi gure 6-2: Sink periodicity and Data Collection Delay ................................................... 91
Fi gure 6-3: Characterization of the applications with mobile sinks .................................. 92
Fi gure 6-4: Feasible region for k with mobile sinks .......................................................... 95
Figure 6-5: Sub-trajectories and collection boundaries ..................................................... 99
Figure 6-6zhnpactonkonTm, Tp and Tu ................................................................... 104
Figure 6-7: Impact of k on trip collection-time for different data rates ........................... 104
Figure 6-8: Data Aggregation at DGs (l-hop nodes of Navigation Agent) .................... 105
Figure 6-9: Impact of k on energy metrics ...................................................................... 107
Figure 6-10: Impact of k on trajectory length, L .............................................................. l 11
Figure 7-1: Logical overlay topology construction for k=2 ............................................ 1 13
Figure 7-2: Network computed TSP path covering NA nodes ........................................ 115

Figure 7-3: Pseudo code for Navigation Agent identification ......................................... 117

Figure 7-4: State machine for navigation and data collection ......................................... 120

xiv

Figure 7-5: Navigation of MDH from NA node F to C (k=2) ......................................... 121

Figure 7-6: Collect-on-Move algorithm description ....................................................... 123
Figure 8-1: 60 node t0pologies used for experiments ..................................................... 128
Figure 8-2: Delay performance for various topologies ................................................... 129
Figure 8-3: Trajectory indices -— collection-time and distance ........................................ 130
F i gure 8-4: MDH trajectories for 60 node uniform topology .......................................... 131
F i gure 8-5: Packet delivery and Collection success index .............................................. 133

F i gure 8-6: Impact of k on average delivery delay .......................................................... 135
F i gure 8-7: Impact of k on collection time and delivery ................................................. 136
F i gure 8-8: Impact of k on hop count and remaining energy .......................................... 137
Fi gure 8-9: MDH trajectories for varying k ..................................................................... 138
Fi gure 8-10: Impact of node density on energy metrics .................................................. 140
F i sure 9—1: Impact of hop-bound k on trip collection-time, t co ,1 in case of Stop-on-Demand
collection ......................................................................................................................... 147
Figure 9-2: Impact of hop-bound k on T m , T p and T u ............................................... 148
Figure 9-3: Impact of hop-bound k on too” in case of Collect-on-Move collection ...... 149
Figure 9-4: Impacts of data rate on stop and stability thresholds .................................... 152
Figure 9-5: Impacts of sink speed on routing thresholds ................................................. 153

Figure 9-6: Impacts of node density on routing thresholds ............................................. 154

Figure 9-7: Impacts of k on ta,” for square network region ......................................... 156

Figure 9-8: Packet delivery performance for varying hop-bound factors (k) .................. 157

Figure 9-9: Delivery and drop performance for varying data rate when (a) k=1, and (b) k:§

- - - . - - . . ................................................................................................................................. 1

Figure 9-10: Variation of Trip collection-time ................................................................ 160

Figure 9-1 1: Variation of stability threshold ................................................................... 160

XV

Figure 9-12: Impact of data rate on the stop threshold .................................................... 162
Figure 9-13: Impact of speed on the stop threshold ........................................................ 162
Figure 9-14: Impact of node density on stop threshold ................................................... 163
Fi gure 9-15: Feasible range for Collect-on-Move mode ................................................. 166
Fi gure 9-16: Feasible range for Stop-on-Demand mode ................................................. 167
F i gure 10-1: Feasible range within non-real-time collection .......................................... 169
F i gure 10-2: Impact of k on trip collection-time (Ce/f < C ) for different data rates ..... 175
FigureIO-3zlmpactofkon Tm, Tp and Tu ................................................................ 176
Fi gure 10-4: Impacts of k on ta,” for square network region ....................................... 177
F i gure 10-5 : Impact of data rate on the stability range and stop threshold .................... 178
Fi gure 10-6: Impact of node density on the stability range and stop threshold ............... 179
F i gure 10-7: Impact of link capacity on the stability range and stop threshold .............. 180

Fi gure 10-8: Impact of MDH speed on the stability range and stop threshold ............... 181

Figure 10-9: Impact of k on trip collection-time ............................................................. 184

Figure 10-10: Impact of k on trajectory length ................................................................ 184

Figure 10-11: Packet delivery performance for varying data rates ................................. 185

F i gure 10-12: Drops experienced by nodes at different hop distance from the Navigation

Agents .............................................................................................................................. 185

Figure 10-13: Impact of data rate on the feasibility range .............................................. 186

Figure 10-14: Impact of density on the feasibility range for different data rates ............ 188

Figure 10-15: Impact of sink speed on the feasibility range ........................................... 189
Figure 10-16: Possible feasible range scenarios .............................................................. 190
Figure 11-1: Network with six regions ............................................................................ 193
Figure 11-2: Data-rate aware regional k selection procedure .......................................... 195

Figure 1 l -3: Trip collection-time for different combinations of regional k values ......... 196

xvi

Figure 11-4: Energy metrics for different combinations of regional k values ................. 197

Figure 11-5: Dr0ps and data accumulation for different combinations of regional k values

- - - - - - ................................................................................................................................... 198
Figure 11-6: Trip collection-time for different combinations of regional k values ......... 199
Figure 11-7 : Example to illustrate the tree rooted at Lambda-Root ................................ 202
F i gure 11-8: Pseudo code for data rate aware NA selection ........................................... 203
F i gure 11-9: Tree rooted at NA-Root .............................................................................. 204

Figure 11-10: NA selection based on data accumulation and the resulting trajectory....205

Figure 11-11: Distance of nodes from the MDH ............................................................. 206
Figure 11-12: Regional breakdown of hop distances to the MDH .................................. 207
Figure 11-13: Comparison of LATD trip collection-time with regionally selected k
tl-ajectories ....................................................................................................................... 208
Fi gure 11-14: Comparison of LATD packet drop and accumulation characteristics with
regionally selected k trajectories ...................................................................................... 209
Fi gure l l-15: Comparison of LATD energy metrics with regionally selected k trajectogies
- - - . ..................................................................................................................................... 10
Figure 11-16: Trip collection-time for different regional data rates ........................ i ....... 211
Figure 1 1-17: Residual data accumulation and drop performance for different regional data
rates .................................................................................................................................. 212
Figure 1 1-18: Energy metrics for different regional data rates ....................................... 213
Figure 11-19: Impact of regional data rates on trip collection-time ................................ 214
Figure 11-20: Data accumulation and drops for varying regional data rates .................. 215
Figure 1 1-21: Energy metrics for varying regional data rates ......................................... 216
Figure A-l: Impact of k on trip collection-time for low data rates .................................. 225
Figure A-2: of k on trip collection-time for higher data rates ......................................... 225
Figure A-3: Impact ofkonTm, Tp and Tu ................................................................. 226
Figure A—4: Impact of data rate on thresholds ................................................................. 226

xvii

Figure A-5: Impact of density on thresholds ................................................................... 227

Figure A-6: Impact of MDH-DG link capacity on thresholds ......................................... 227
Figure A-7: Impact of speed on thresholds ..................................................................... 228
Figure B-l: Trajectory length computation for sample covering trajectories ................ 228

xviii

CDC
COM
CSA
DG
EAR
EASP

EDR

LATD
MAPC
MDH
MST
NA
NADC
PDC
RN
RW
SCAV
SDRE
SOD
SPR
SPR

TSP

List of Abbreviations

Continuous Data Collection
Collect-on-Move

Centralized Swapping Algorithm
Designated Gateway

Energy Aware Routing

Energy Aware Swap Protocol
Energy Drainage Rate
Intermediate Navigation agent
X—Aware Trajectory Determination
Mobility Assisted Power Control
Mobile Data Harvester

Minimum Spanning Trajectory
Navigation Agent

Network Assisted Data Collection
Periodic Data Collection
Run-time Navigation

Random Walk

Swarm Capable Autonomous Vehicle
Standard Deviation of Remaining Energy
Stop-on-Demand

Shortest Path Routing

Shortest Path Routing

Travelling Salesman Problem

xix

 

UAV Unmanned Aerial Vehicle

UGV Unmanned Ground Vehicle

XX

Chapter 1. Introduction

1.1 Wireless Sensor Networks

Developments in wireless and mobile communications combined with the constant
advancements in electronics that enable the integration of complex components into
smaller devices, have contributed to the emergence of a new class of wireless ad hoc
networks — sensor networks. A sensor network consists of a large number of small,
low-cost devices with sensing, processing, and transmitting capabilities. A sensor board
mainly consists of one or more sensors, a microprocessor and a low-power radio
transceiver. The on-board sensors may be motion detectors, therrnistors, light sensors,
microphones, accelerometers, magnetometers, humidity and barometer sensors, GP S
receivers, etc. Sensor networks are expected to be used for a wide variety of applications
such as surveillance, habitat and environmental monitoring, target tracking, search and
rescue, disaster relief and so on.

Sensor networks [1-3] are expected to be used in regions where there is no
infrastructure support. Sensors are usually deployed in large numbers often in
inaccessible or hostile regions. Hence, they are expected to operate untethered and
unattended for long periods of time. Sensor nodes are powered by batteries and therefore,
they tend to be extremely energy constrained. Replacing the batteries or recharging them
When the network is in operation can be very expensive or impractical depending on the

terrain. For this reason, energy efficiency and network lifetime elongation are important
considerations while designing protocols for sensor networks. Energy consumption for
communication is several orders of magnitude higher than the energy required for

computation [4]; and wireless communication is foreseen to continue to dominate the

l

energy consumption in a sensor node in the near future. To address this, a lot of research
effort has centered on minimizing the amount of communication that is required during
sensor network operations.

Protocols that have been developed for traditional ad hoc networks may not be suitable
for sensor networks. This is because sensor networks present certain unique differences
and challenges that require specific design efforts[2, 5].

Applicgtion driven requirements: Sensor networks are expected to be deployed for very
specialized and specific applications such as environmental/habitat monitoring, public
facility monitoring, health monitoring, target tracking, battle-field surveillance and
intrusion detection, which all benefit from combination of sensing, computing and
communication technology. Each application is likely to have different performance
objectives. Providing bounded message latency and high delivery ratio may be crucial in
certain timing constraint applications such as intrusion detection and disaster warning.
However, for some other applications like environment monitoring it is enough to have
only occasional packet delivery but long lifetime.

Resmce Constraints: Sensor nodes are powered by battery and replacement is usually
impossible in many applications. Thus, energy supply is scarce and energy consumption
is a primary consideration for wireless sensor network protocol and algorithm design.
Since communication is significantly more expensive than computation [4], reducing
energy dissipation used in communication becomes very important. Typically the

Han-essential energy consumptions are contributed fi'om four sources. (1) Protocol
overhead: besides valid data certain control messages have to be exchanged to access

communication resources; (2) Message collision: unnecessary additional energy is wasted

by retransmitting as well as possible duplicate receiving same message; (3) Overhearing:
sensor nodes spend energy to receive messages for which they are not intended receivers
because that message transmission usually is broadcast within WSN; (4) Idle listening:
when there is no centralized scheduler to manage sequence of transmission, sensor nodes
have to keep the wireless radio interface up (power on) for possible message receptions.
Even when sensor nodes do not transmit or receive any message the consumed energy
can be significant. Researchers [4, 6] have shown that idle listening actually accounts for
most of the energy expenditure in low traffic situations, which is quite common for most
applications in WSN.

Mm: Although wireless sensor networks are often considered as stationary, some
applications may require one or several powerful mobile nodes to achieve better
performance or other purpose [7, 8]. Some applications like mapping[9, 10],
mine-removal [11], etc., require mobile sensors that are capable of moving to accomplish
their primary mission. Mobility may be random (ex. animals [12, 13], humans),
predictable (ex. public transport vehicle) [14] or controlled by the network (ex. robotic
sensors [15]).

Mobile sensor networks or robotic sensor networks [16] [17, 18] have enormous
potential for positive impact in our society. They can be employed for search-and-rescue
operations in emergency situations. In environmental monitoring, they can be used to
detect forest fires or monitor rare species. In health care, they can monitor patients or the

elderly over extended periods of time without confining them to a small area. A number
0f automation tasks, such as surveillance, monitoring energy consumption and quality

inspection, can benefit from robotic sensor network technology.

Traffic Patterns: Unlike communication in traditional ad hoc networks based on
point-to-point connections, sensor networks mainly use a broadcast communication
paradigm and almost all applications of sensor networks require the flow of sensed data
from multiple sources to a particular base station. This, however, does not prevent the
flow of data from being in other forms (e. g., multicast or peer to peer). The reason that all
messages are sent to all the radio neighbors is because a single sensor node lacks global
knowledge of the whole network. The traffic in the sensor networks can be continuous,
event-driven, query-driven, or hybrid based on applications. A low data traffic rate over a
large time scale but with some very bursty traffic sometimes can be quite a common
situation in the wireless sensor networks.

Data Genfltion: Sensor nodes may generate data in the following manner.

a. Continuous: Data is generated continuously and routed to the sink. For instance,
habitat monitoring requires sensors to monitor physical parameters such as
temperature, humidity, etc., of a geographical region periodically and route that
information to a base station.

b. Event-driven: Data generation by a sensor is triggered by an event of interest.
Only sensors around the event generate data that has to be sent to the base station.

c. Demand-driven: Data is sent to the sink in response to an explicit request that is
propagated into the network in the form of a query [19].

Data Centric: The random deployment of sensor nodes ofien introduces a redundant

no dal distribution. The redundancy is desirable for reliable coverage of the sensor field
With low-cost and low-energy sensor nodes. Moreover, data collected by many sensors in

WSN is typically based on common phenomena, so there is a high probability that this

 

data has some redundancy. Such redundancy needs to be exploited by the routing
protocols. Attribute-based naming for example, can be used to improve both energy and
bandwidth utilization. In WSN, sometimes getting the data pertaining to sensor node
observation is more important than knowing the IDs of which nodes sent the data. As a
result, the importance of an individual node is reduced in contrast to traditional networks.
This changing of role makes the design change from a node-centric to a data-centric
architecture.

Location Awareness: Position awareness of sensor nodes is important since data
collection is normally based on the location. Currently, it is not feasible to use Global
Positioning System (GPS) hardware for this purpose. Methods based on triangulation
[20], for example, allow sensor nodes to approximate their positions using radio strength
from a few known points. According to [20], algorithms based on triangulation or
multilateration can work quite well under conditions where only very few nodes know
their positions a priori. Still, it is preferable to have non-GPS solutions [21] for the

location problem in WSN or to design protocols/algorithms that are location independent.

1.2 Applications of Wireless Sensor Networks

Initially, sensor networks were motivated by defense applications such as intrusion
detection, battlefield surveillance and object tracking. Since then, there has been a rapid
increase in the number of civilian applications like environment data collection, health
monitoring, vehicular sensor networks, disaster relief and the like.

Sensors are expected to be deployed in large numbers for monitoring applications.
These applications can be broadly classified as continuous monitoring and event

monitoring. Continuous monitoring applications are typically used for scientific research

and require nodes to generate sensing data periodically over a large period of time.
Using sensor networks enables data to be monitored at a scale and resolution that is
difficult to achieve using traditional monitoring technologies. The data is used to analyze
long term trends and spot interdependencies. On the other hand, event monitoring
applications are intended to detect the occurrence of pre-defined events in the sensing
environment, for instance, detecting intrusion, temperature below a certain threshold, etc.
Therefore, sensors may sense their environment periodically, but they do not constantly
generate data destined for the sink.

1.2.1 Continuous Monitoring

f‘iorjzont' ‘ '1

'l‘rarrspoi
. l"

Ti'anspo ‘

Module U

Sensor \odes

I .-
;‘. '.

Vertical
Sensor Node «

 

Figure 1—1: NIMS deployment in James San J acinto Mountains Reserve

(source: http://research.cens.ucla.edu/areas/ZOOS/NIMS)

There are a number of projects[13, 22, 23] that have deployed sensor networks for

continuous monitoring applications. [24] provides a comprehensive list of environmental

applications. In this section, two examples using mobile sensors/elements are presented
to outline the general nature of these deployments and their features.

The Networked Infomechanical Systems (NIMS) [25, 26] project at UCLA integrates
distributed, embedded sensing and computing systems with infrastructure-supported
mobility to enable direct uncertainty characterization, autonomous adjustment of
spatiotemporal sampling rate, and active sensor fusion. It has deployed robotic sensors
suspended from cables which can move along the cable. The cableway system allows
controlled mobility in horizontal and vertical directions in an energy eflicient and precise
way. In contrast to wheeled and tracked vehicles, the mobile element here is suspended as
shown in Figure 1-1.

The NIMS test bed has been deployed to collect dense environmental and ecological
data about populations of rare species and their habitats within a mountain stream
ecosystem and the surrounding conifer forests and meadows. The sampling protocol that
allows NIMS to collect and bring samples to the lab autonomously will allow researchers

to monitor and measure short-lived, intense events in the environment as they occur.

 

Figure 1-2: NAMOS at Lake Fulmor and a robotic boat
(source: http://robotics.usc.edu/~namos/gallery.html)

Figure 1-2 shows a Networked Aquatic Microbial Observing System (NAMOS) [25,
27, 28] deployed at Lake Fulmor. Buoy nodes are deployed across the surface of the lake
and provide a spot measurement of temperature and chlorophyll. This data can be used
for an estimation of these parameters in the complete extent of the lake. The robotic boat
[is guided by the buoy data that provides not only a reconstruction of field variables, but
also a directive that determines the location of NAMOS deployment providing the
greatest enhancement in fidelity of reconstruction.

1.2.2 Event Monitoring

Battlefieflurveillance: Important terrains, routes, and paths can be monitored by
deploying sensor nodes to the dedicated field [29, 30]. Sensor nodes can sense the
activities or predefined critical events, and send raw or post-processed information to the
base station. Real-time situation requests can also be generated at the base station, and
disseminated to the requested area.

Target Tracking: Collaborative mobile sensing can cooperate with a navigation system.
For example, networked tracking of adversarial mobile targets using unmanned ground
and airborne sensors is an emerging enabling application for the militaries’
network-centric warfare capability. Target tracking [31, 32] is a prevalent military
requirement across a wide spectrum of surveillance and reconnaissance scenarios
including those in remote and urban battlefields, inside large buildings and public places

such as airports, train stations, and inside underground tunnels.

1.3 Performance Metrics

1. Energy and Network Lifetime

The operating or functional lifetime of a network is an important performance metric

for sensor networks. In the literature, various definitions for network lifetime have been

8

considered — time until death of the first node, loss of coverage occurs in an area, a
certain percentage of nodes die, or network becomes partitioned and hence useless.
However, most of the studies use time until the death of the first node as the decisive
factor. It is important to note that when the network becomes ineffective because of the
death of a few crucial nodes, other nodes may still have a considerable amount of residual
energy. Theoretically, maximum lifetime is achieved when all nodes in the network die at
the same time - i.e, the amount of energy remaining in the network is zero. Thus, the
total amount of residual energy remaining in the network is indicative of the extent of
longevity that could be achieved.

Network lifetime is a joint result of two energy metrics [33], namely, the energy
dissipation rate of the nodes and the variance of energy dissipation rate across the nodes.
Thus, reducing the energy dissipation rate or distributing the load uniformly across the
network could be beneficial to the longevity of the network.

2. Data Delay

Delay incurred by data packets generated in the network is another important metric
especially for event monitoring or surveillance type of applications where an event that is
detected has to be relayed to the base station with the lowest delay possible. In habitat or
environmental monitoring type of applications, it is less important. Nevertheless, even in
continuous monitoring applications, there is usually an upper bound on the delay that can
be tolerated.
3. Accuracy

In continuous monitoring applications where a large amount of data is periodically

generated, researchers have considered aggregating data from multiple nodes with an

acceptable compromise in' data accuracy. Accuracy can also be affected by packet
delivery performance of the routing protocol. The larger the packet loss for a sensing
area, the lower the accuracy.
4. Scalability

Sensor networks are expected to be large in terms of the number of nodes deployed.
Thus, scalability is an important performance criterion for protocols and algorithms that
are developed.
5. Fault tolerance

Sensors are energy constrained and thus are more prone to failures. Also, the nodes
are expected to be deployed in hostile terrain and hence can be exposed to various
physical, chemical and biological forces leading to failures. Hence, fault tolerance is an

important performance metric.

1.4 Energy in Sensor Networks

1.4.] Sources of Energy Expenditure

In a sensor network, nodes incur energy cost for sensing, processing the sensed data
and transmitting the data to the base station. Radio communication is considered to be the
single biggest source of energy consumption when compared to sensing and processing
[2].

Therefore, researchers have focused on devising energy efficient strategies for saving
energy at various layers. The radio interface uses energy when it transmits, receives or is
idle. The energy required to transmit is usually the highest. According to the MicaZ
sensor platform specifications [34], the current draw for the radio interface is as follows:

27mA for transmission with maximum power, IOmA for receive and < luA for sleep.

10

The transmit energy of a node is expended due to the transmission of its own data
towards a base station and routing data generated at other nodes. In networks with
uniform data generation rate, the variation in communication energy drainage across
nodes is dictated primarily by their individual routing loads.
1.4.2 Energy Conservation Mechanisms

As explained above, the operating lifetime of a sensor network is highly dependent on
the rate at which nodes drain their energy. Thus, a lot of research effort has been focused
on designing energy efficient mechanisms at all protocol layers: physical, medium access
control, routing and application. In order to prolong network lifetime, different strategies
should be adopted at all network layers including hardware improvement at physical
layer, energy efficient medium access control protocol, topology control and routing.
These strategies include avoiding packet collision [35] and idle listening [36], limiting
carrier-sense energy consumption [37], optimal routing path selection and content
filtering or error tolerance [3 8, 39] and data aggregation [40] at application layer.

In order to reduce the idle listening and up time of radio interfaces, sleep scheduling
[41] has been studied in low data rate sensor networks. To reduce the transmission energy,
several power-control-based schemes have been proposed. Since communication energy
typically dominates the nodal energy budget, it is possible to extend network operating
life by evenly distributing the data routing load across the network. Removal of routing
hot-spots by load distribution can be achieved by deploying constrained-routing schemes
[42-45] that aim to balance network traffic based on remaining nodal energy [46], link
power budget [47, 48], or a combination of both [42]. Cluster-based routing protocols

reduce energy consumption by data aggregation and balance energy consumption by

11

rotating clusterheads [47, 49, 50]. Topology control algorithms [51-54] that attempt to use
minimum transmission power than can guarantee the required level of network
connectivity have also been studied extensively. While these mechanisms work
reasonably well, they have limited effectiveness in networks with insufficient path
diversities. Moreover, the energy-constrained routes tend to be longer, and hence they
incur higher delivery latencies [43] and overall energy consumption on a per-packet
basis.

Recently, the use of mobile elements for enhancing various network performance
metrics has started to gain attention. The mobility of these mobile elements is
controllable and planned to achieve desired performance objectives - especially energy
savings for sensor nodes. The use of mobile elements does not preclude use of other
energy saving mechanisms that have been used at the MAC, routing and other layers.

Thus, controlled mobility can further enhance energy performance.

12

Chapter 2. Controlled Mobility and Network Performance

2.1 Introduction

Until recently, researchers have primarily focused on networks where sensor nodes and
the base station are assumed to be static. Even when mobility was considered, it was
random and unpredictable in nature. Therefore, mobility was an extra overhead to which
the network must adapt, possibly at a loss of performance.

With progress in technology, diverse sensing and mobility capabilities will become
more readily available to devices. Many mobile robots are capable of various sensing
capabilities. It is not difficult to imagine large systems of such mobile autonomous
agents [55] or hybrid networks consisting of static nodes and mobile elements performing
various tasks in the near future. In this context, researchers have started investigating
ways of exploiting controlled mobility to enhance network performance. Controlled
mobility refers to the ability of one or more components in a sensor network to move
autonomously. Components that are capable of mobility are more expensive than
ordinary sensor nodes in terms of energy, hardware resources and navigational needs.
Adding infrastructure components such as cableway or track along which the network
elements may move has been proposed. Such components drastically reduce the
overheads that are incurred by mobility.

Controlled mobility in a sensor network mitigates many issues:
a. Network deployment: Self deployment becomes easier if sensors are able
to move to desired locations to improve coverage [56].
b. Fault tolerance: Sensor nodes could move to locations to ensure increased

connectivity and avoid routing holes in the event of node deaths [57-61].

13

. Energy harvesting: Mobile elements could potentially physically supply

energy to sensor nodes fiom areas where energy is available [58, 62].

. Adaptive behavior: Controlled mobility enables the network to be reactive
to events in the network or to events in the monitoring area in surveillance
applications [63, 64].

Concurrent missions: Mobile elements make it possible to support
concurrent disparate missions with a common set of sensors [61, 65, 66].
Localization: Beacon from a mobile element can be used for localizing
the static nodes in a sensor network [67].

. Data delivery in disconnected networks: Mobile elements can help to
deliver data between disconnected or intermittently connected network
partitions [68-70] [8, 59].

. Network longevity: Longevity of sensor networks can be increased by
incorporating mobile elements in the network — mobile sinks, mobile

relays or mobile sensor nodes [71-80].

2.2 Problem Definition: Lifetime Limitation in Static Sensor Networks

In sensor networks deployed for monitoring applications, nodes generate data

periodically at a constant rate and send the data to one or multiple base stations. Typically,

multi-hop routing is used to route the data. Thus, the amount of energy dissipated by the

nodes in the network depends on their distance from the base station. The closer the node

to the base station, higher the energy dissipation rate [81]. Figure 2-1 :a shows an example

monitoring network which is periodically routing data to two base stations located on the

periphery of the field. Figure 2-1:b shows the energy dissipation rate of the sensor nodes.

14

It is clearly evident that the nodes in the immediate neighborhood of the base stations
drain energy at a significantly higher rate compared to the rest of the network. This is due
to the fact that first-hop nodes or gateway nodes have to forward messages originating
from many other nodes in addition to their own messages. Thus, when the gateway nodes
run out of energy, they cause a partition of the network, making the sensor network
non-fimctional, limiting the lifetime of the network. Consequently, the lifetime of the
network is directly related to the longevity of the first-hop nodes. This is a fundamental
limitation for sensor routing solutions involving multipoint-to-point data models.

The routing of the sensed data to the base station is one of the most energy intensive
operations in a sensor network. In the interest of network longevity, therefore, it is
important to ensure that periodic data collection is performed in the most energy efficient
manner. If the base station is static, then the most energy efficient solution is obtained by
placing the base station in the most optimal position. While this results in lifetime
improvement over an arbitrarily placed base station scenario, it still suffers from high
energy drainage rates around the base station, as shown in Section 2.2. To address this
issue, researchers have proposed the use of mobile elements in the network whose
mobility can be controlled for the purpose of enhancing performance objectives such as

energy efficiency and network longevity.

15

 

Data Aggregator
(Base Stgtion)

Ad Hoc Robotic
Sensor\Network

 

  

Sensing Tiles

 

b

z-x
_)
v
>\
U)3
L
0
c
LU

Dissipated Energy (J)

   

 

(b)

 

 

Figure 2-1: a) Sensor network with multi-hop data collection b) Energy drainage profile

2.3 Controlled Mobility for Data Collection

Figure 2-2 shows the possible solutions for data collection in sensor networks.

Controlled mobility solutions that have been preposed fall into one of the following three

application categories depending on the elements which are mobile.

 

 

Sensor Network Data Collection

\

Optimally Placed Static Sink

\

 

 

 

 

 

 

 

 

 

 

Controlled Mobility

 

 

 

 

, Static Sensors,
Mobile Sensors Stat; fiensgrskand Static Sink and
and Static $an 0 re '" Mobile Relays

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2-2: Design options for data collection in sensor networks

First are networks where sink or base station is static, but the sensors are capable of
mobility. Mobile robotic networks [82, 83] comprising teams of mobile robots,
underwater vehicles, Unmanned Ground Vehicles (UGVs) or Unmanned Aerial Vehicles
(UAVs) are examples. The mobility of these devices can be controlled by the network.
Second are networks where sensors are static and the base station or sink is mobile. In
this case, the trajectory of the base station can be planned to meet network objectives.
The last type is networks where sink and sensors are static, however mobile elements
called relays move such that they visit nodes and temporarily take on the responsibilities

of the host node for a certain period of time.

2.4 Related Work
Existing literature pertaining to controlled mobility in each of the three categories

discussed in Section 2.3 and shown in Figure 2-2 is presented in this section.

17

2.4.1 Mobile Sensors and Static Sink

The proposals in this category are aimed at networks which consist of mobile sensors
such as networked robotic sensors capable of at least limited mobility. In this solution, the
sensor nodes execute a controlled and concerted mobility sequence for extending the
network life by distributing routing energy overhead across the network. The only
solution in the literature that proposes such a mobile-sensor-based approach reduces
energy consumption by adjusting [84] the positions of the intermediate nodes on a
per-route basis from each sensor to the base station. The key idea is to move intermediate
nodes on a route such that they are evenly spaced on the line between the source and the
destination. Such a node placement has been proven to minimize transmission costs
incurred by nodes when power control is used. The main emphasis of this mechanism is
to extend network lifetime by reducing the energy spent per delivered data packet. The
cost and benefit of mobility is taken into account when a mobility decision is made.
This idea was further developed in [85] with the development of protocols and algorithms
required to locally collect and share the cost and benefit information of movement to
allow nodes to make local mobility decisions.
2.4.2 Static Sensors and Mobile Sink

The key idea behind mobile sink is that the nodes around the sink change over time and
therefore the energy drainage due to routing occurs more uniformly, thereby extending
the network life. Applications involving umnanned aerial (UAV) and ground vehicles
(U GV) as mobile sinks have gained popularity particularly for networks deployed in
adversarial and hard to reach terrains. In addition to the network life and collection delay,

the time for which a UAV or UGV is exposed to threats during data collection also acts as

18

a critical design constraint.

The research work pertaining to mobile-sink-based data collection can be classified into
two categories: real-time and non-reaI-time collection. In the real-time case, the mobile
sink moves along a trajectory visiting various nodes in the network in a pre—determined
order. While the sink is visiting a node, all other sensor nodes route their data to the sink
at its current location. The research challenge here is to jointly optimize the sink
trajectory and the packet routing paths from each node to the mobile sink. Note that the
delay that is incurred by data packets is in the time scale of packet routing similar to the
static sink scenario. Therefore, this data collection scenario is referred to as real-time.
Since the nodes need to route data to the sink at its current location, higher control
overhead is required to maintain route information to the sink.

In non-real-time collection, the mobile sink collects data from a subset of nodes
referred to as Designated Gateways (DGs). The rest of the sensor nodes route their data to
one of the DGs where it is buffered until the mobile sink comes into the vicinity of the
D6 The key research problem here is to jointly optimize the trajectory (includes the
choice of designated gateways (D63) and the order in which they are visited) and the
packet routes from the sensor nodes to the DGs. Thus, the delay for the data packets is in

the time scale of mobility and hence the scenario is referred to as non-real-time.

2.4.2.1 Real-time

In the existing literature that addresses the problem of data collection by a mobile sink
to meet real-time application requirements, there are three distinct categories: the first
addresses the problem of routing to the mobile sink [86-90] for a given trajectory, the

second focuses on optimizing the trajectory for a given routing scheme [91-93] and the

19

third attempts to jointly optimize mobility and routing [71].

 

 

Static Sensors and Mobile Sink

/ \

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

R e al-tim e Non—.real—time

Monitoring Monrtorrng

Research Problem: / \

Joint sink scheduling Single-Hop .

and packet routing to Multl-Hop

sink Research Problem:

Sink scheduling Research Problem:

Joint sink scheduling
and packet routing to
DGs

 

 

 

 

 

 

Figure 2-3: Solution classification for mobile sink data collection

Routing to a mobile sink is a challenge because of the cost of maintaining paths to the
sink from every sensor node. There is a tradeoff between the need for frequent re-routing
to ensure optimal network operation and minimizing the overhead of topology
management. Two approaches have been taken to route to the mobile sink. As the sink
moves, the nodes with first-hop connectivity with the sink change. Therefore, the sensor
nodes could continuously establish new paths to the sink [87]. The authors [87] propose a
distance-vector-approach-based routing protocol called MobiRoute. Route messages are
exchanged periodically among neighbor nodes, and the next hop nodes are chosen by
evaluating the costs of routing data through different neighbors. This results in high
control overhead and high data loss as paths are torn down and re-established, especially

if the sink is highly mobile.

20

To avoid excessive control overhead that results from rerouting paths, alternative
approaches have been explored. The first approach entails extending the paths of existing
routes by adding hops locally to route packets to the sink at its new location. Paths to the
sink are occasionally rerouted [88] when they become overly inefficient. The second
approach is to designate a gateway to relay data to the mobile sink from the sensor nodes.
When the sink moves to a new location, the designated gateway establishes a path to the
mobile sink and continues to route packets to the sink node using triangular routes [86].
These routes are gradually optimized over time. The incremental route optimization
scheme presented in [86] is called Net Cast Routing (NCR). The third approach is a
learning-based approach to route data efficiently to a mobile sink [89]. In the scheme
presented in [89], first-hop nodes learn the sink’s movement pattern over time and
statistically characterize it as a probability distribution function. So, the sensor nodes
make a decision about the next hop based on the time at which the data packet is
generated. In the first and second approaches, as the sink mobility increases, data latency
goes up due to longer paths to the sink or due to control overhead in addition to data loss
that could occur when the route is re-established. Obviously these approaches do not
scale well when the sink is very highly mobile. The third approach does not work well in
networks where the mobile sink changes its trajectory frequently.

A few papers deal with the problem of trajectory optimization for a certain routing
scheme. In this approach the sink moves from one location to another from amongst a set
of feasible locations and stays there for an extended period of time. Nodes that are not in
direct contact with the sink use multi-hop paths to route data to the sink. The trajectory

computation problem involves computing the duration of time that the mobile sink needs

21

to stay at each location, also referred to as sojourn time at each location. A linear
programming formulation of the problem for obtaining the sojourn times is provided in
[91] [94]. In this formulation the set of feasible locations is the same as the locations of
the sensor nodes themselves. Also, it assumes that the movement fiom one sink location
to another is instantaneous and that the data generation rate and initial energy are uniform
across all nodes. An improved linear programming formulation that takes into account the
problem of finding energy-efficient paths apart from the sojourn times has been presented
in [93]. However, the feasible sink locations are restricted to a few hand-picked locations
in the sensor field, unlike [91] where all node locations are potential candidates.

In [71], authors use heuristics to solve the joint routing and sink mobility optimization.
They conclude that the best trajectory for a mobile sink is the periphery of a circular
sensor field. The suggested routing strategy is to route to the edge of the network using
shortest path routing and then to follow the edge to the current location of the sink,
referred to as round routing. This paper does not explore the protocol aspects of this
scheme. Also, it assumes a uniform data generation rate across time and nodes and
assumes a circular sensor field. The best trajectory suggested by this solution may not
necessarily apply to any arbitrary topology. In practice, implementing round routing
requires the sink and the sensor nodes to have location-awareness.

The above-mentioned trajectory computation approaches assume a fixed routing
scheme or limit the number of feasible locations. Also, they assume that the sink is
likely to have long sojourn times at the feasible locations. Therefore, they do have
mechanisms to alter sink trajectories based on any dynamic parameters such as changing

data generation rates of nodes in the network. A distributed implementation of [91] has

22

been presented in [92]. [95] also presents a similar solution. The protocol presented,
called GMRE' (Greedy Maximum Residual Energy) [96], greedily selects the site within a

certain dmax surrounded by nodes that have the most energy left. After spending a

time tmin, the sink evaluates whether to move toward one of its adjacent sites. In order to

decide if it should move, the sink gathers residual energy information of nodes around the
potential future sites. If there are sites with higher residual energy than at the current site,
the sink moves. During the duration of the sink movement from the current site to the
next, the packets are buffered at the first-hop nodes. This increases the data latency. In
the proposed solution fi'amework, the handoff mechanism ensures that the latency

incurred by data packets is lower as there is no buffering of packets involved.

2.4.2.2 Non-real-time

Single-Hop:

This long-lifetime and high-delay data collection is particularly suitable for delay
non-critical applications in networks with long unattended operating life. The primary
research problem is to compute the trajectory with minimum possible round trip time
while collecting data in a single hop. For applications with heterogeneous data generation
rates and limited buffers at the sensor nodes, the sink has to visit the nodes before their
buffers overflow in order to minimize data losses [97, 98].

The existing literature in this area primarily addresses the continuous non-real-time
applications using centralized trajectory computation and random-walk based
mechanisms. The centralized trajectory formulations pose the sink scheduling problem
either as a special case of a Vehicle Routing Problem (VRP) [97], a Traveling Salesman

Problem (TSP). [98-101] or a Label Covering Tour problem in [102] or a

23

cluster-formation problem [103, 104]. While providing efficient trajectories, all these
solutions assume the availability of sensors’ geographical location information, and
therefore, would not apply to networks without localization services. A number of
random-walk-based single-hop collection mechanisms without sensor localization are
reported in [105, 106]. The main drawback of these random-walk-based approaches is
their inability to deterrninistically collect data from all sensor nodes. In addition to
coverage problems, this also leads to unbounded packet delivery delays.

Multi-Hop:

If the sensor network application is delay tolerant, then data fiom the nodes does not
necessarily have to be routed to the current location of the sink. Instead the data can be
buffered at the Designated Gateway (DG) nodes until they come in contact with the sink.
This obviates the need to reroute paths constantly to the current location of the sink. In
this paradigm, a node routes packets to the closest gateway node. Data collection using a
mobile sink that moves through a sensor field along a fixed track is presented in [7, 73].
Although the path is fixed, the mobile sink has the ability to vary its speed. The paper
presents different motion control algorithms for the sink including fixed speed movement,
variable speed based on data rate and data priority. The extent of multi-hop routing here
is dependent on the location of the fixed track with respect to the topology. Although this
achieves better lifetime than the static-sink approach, due to the static nature of sink
trajectory the energy depletion is eventually experienced by the DGs or nodes along the
trajectory. The paper also describes the modifications that can be made to Directed
Difiusion to route packets to the hop-wise closest gateway nodes. If the sink moves out of

the transmission range of a gateway node before all the buffered data is transferred to the

24

sink, the data may be dropped. In order to overcome the scalability and data loss
problems that can occur when using a single mobile sink moving along a fixed path in a
sensor field, [107] proposes using multiple mobile sinks. It also provides a load balancing
algorithm that the multiple mobile elements can use.

In [108], a joint trajectory and route optimization problem has been formulated in
which for a given MDH speed and a target delay deadline, “rendezvous points” are
chosen such that the time required to visit them meets the target delay while minimizing
the energy required to route data to the rendezvous nodes. This formulation assumes that
the time required to upload data from the rendezvous points is negligible. In other words,
the wireless capacity constraints are not taken into consideration. A random-walk based
scheme with limited multi-hop routing was proposed in [106]. Although it performs
better than the single-hop version, the general concern about coverage still remains. In
contrast, the distributed framework presented in this paper can collect data with complete
node coverage while minimizing the delivery delay as indicated by the results in Chapter
9.

Test-bed experimental results showing that employing mobile sinks for data collection
can help energy and lifetime performance are presented in [109]. In addition, researchers
have studied the use of mobile devices for ferrying data amongst sensor nodes in a
disconnected network [110] [68]. The idea is to use mobile elements whose movement is
controlled such that the network throughput is enhanced. Use of multiple mobile
elements and designing their trajectory has also attracted some attention [111] [112]. In
this thesis, we restrict our attention to connected networks with one mobile sink.

2.4.3 Static Sensors, Static Sink and Mobile Relays

25

Mobile sensor based and mobile sink based solutions do not apply in networks where
both the sensors and the base station are static. Or, in hostile terrain, it may not be
possible for a base station to be mobile. In such situations, hybrid networks have been
considered where base stations and most sensors are static. One or more energy-rich
mobile nodes capable of controlled mobility called ‘mobile relays’ are added to the
network to act as relays for the data [72, 74]. Mobile relays are different from the mobile
sink in that they are not data collection devices. They merely take over the
responsibilities (sensing and communication) of the nodes on their trail and enable the
nodes to rest for a certain duration. This could help bottleneck nodes to conserve energy
and thereby increase the network lifetime. The research issue in this paradigm is to
determine the amount of time that the mobile relay will spend at each sensor location in
the network. Then, while the mobile relay is at a node’s location (say node i) for'a
duration say d, it inherits the responsibilities of node i and allows it to rest. The routes
used by the sensor to send data to the sink may remain unchanged (static routing) or
could change to utilize the mobile relay as much as possible (dynamic routing).
Dynamic routing leads to higher improvement in lifetime as more packets pass through
the mobile relay and hence more energy savings is achieved by the sensor nodes in the

network.

26

Chapter 3. Thesis Scope and Contributions

The focus of this thesis is to investigate frameworks that can help to leverage mobile
elements in sensor networks to improve performance, especially lifetime and various
energy metrics. The basic goal is to explore joint mobility and packet routing
optimization and in the process understand the tradeoffs that are involved to achieve the
better performance. Based on the insights gained about the tradeoffs, system design
guidelines are developed for given application and network parameters. Solution
frameworks for two categories - namely, networks with mobile sensors and static sink
and static sensors and mobile sink - have been considered in this thesis. Figure 3-1

shows a visual summary of the scope of the work presented in this thesis.

 

Joint Mobility and Packet Routing in Sensor
Networks for Performance Optimization

 

  
 
 

   

 

  
      
     
     

Energy Aware
Node Mobility and
Routing

Hop-bound based Ge@
I Framework _

nergy-Aware Swapping
Protocol (EASP)

(Chapter 5)

(Chapter 7)

Network-Assisted Data Stability Analysis
Collection (NADC of Mufti-hop Data

(Chapter 8) I Collect/on Link Capacity
(Chapter 10) \
EASP Performance
NADC Performance
Evaluation

Evaluation Capacity-A ware

   
     

  
    

---------_-----
-_----__-—--‘—--_--~

Extensions to Stability Analysis
(Chapter 6) Heterogeneous /
(Chapter 9) Networks (Chapter 11)
\ Mobile Sensors 8. Static Sink ; ‘\\ (Chapter 12) ,’
‘ e g g / ~ , ‘ Static Sensors & Mobile Sink , , ’

Figure 3-1: Pictorial view of the scope of the thesis

27

\
Sink Trajectory Planning and \\
Packet Routing

3.1 Collaborative Node Mobility for Energy Conservation

The motivation for this work is to leverage sensor mobility to achieve better network
lifetime. In traditional static networks, network lifetime is limited by the energy
reserves of the gateway nodes as pointed out in Section 2.2. When the network becomes
partitioned, the network collectively still possesses a large amount of energy. The existing
literature [84, 85] in this area attempts to increase the network lifetime by reducing
energy spent per packet. The goal of this thesis is to investigate a different approach,
namely to employ controlled mobility to evenly spread the routing burden and thereby
increase the lifetime. The main emphasis of our mobility control framework is to achieve
energy load distribution without significantly altering the packet transmission cost. In this
new approach, energy consumption across the sensor nodes is distributed by executing a
cooperative mobility mechanism based on localized energy information at the nodes,
while the base-stations remain static. The idea is to extend network life without incurring
additional delivery latencies (unlike the energy constrained routing mechanisms, which
give rise to longer paths), especially in networks with limited path diversity.

In this thesis, the uneven energy drainage problem of sensor networks is mapped to a
biological problem in penguin colonies. Inspired by the principles of the biological
solution, a framework is proposed within the constraints of the sensor network resources.
The proposed solution recognizes that energy cost of physical mobility is several orders
of magnitude higher than communication cost. Thus, it attempts to minimize the amount
of physical movement required to achieve longer lifetimes. The mobility pattern required
to achieve the maximum lifetime is analytically derived. Also, a distributed framework is

proposed that allows nodes to make a decision about movement based on local energy

28

information and to implement the physical movement. The protocols developed resolve
contention with respect to chosen movement and take care of handoff requirements to
ensure that node mobility does not affect network layers significantly. The solution
framework was implemented in simulation and the performance of the distributed

algorithm is compared with a centralized version.

3.2 Generalized Framework for Mobile Sink Trajectory Planning and Routing

Data collection using energy-rich mobile elements has been proven to increase lifetime
of sensor networks. In the literature, researchers have studied single hop and multi-hOp
data collection separately. In this thesis, the goal is to provide a generalized framework
for non-real-time data collection using mobile sinks and to design distributed algorithms
and protocols required to implement the generalized fiamework in practice.
3.2.1 Generalized Problem Formulation

Figure 3-2 shows the collection delay and lifetime performance of the solution space
for mobile sink data collection. Note that since mobile sinks are considered energy-rich,
the Cost of their mobility is not considered in the problem formulation. The lowest-delay
solution involves data collection using an optimally placed static sink [113-116]. The sink
needs to be placed such that the average delay from all sensors to the sink is minimized.
This solution, however, suffers from the worst possible network life due to the expedited
energy depletion of the gateway nodes around the static sink as mentioned in Section 2.2.
The opposite scenario - best lifetime with worst delay - can be achieved when the sink is
programmed to periodically visit each node in the network for single-hop data collection.
Maximum lifetime is ensured because of zero routing energy expenditure, and the

maximum delay is caused due to the fact that the sink has to physically move to

29

individual sensor nodes, and therefore the trip time will be the maximum.

Real-time event monitoring using a single dynamically chosen Designated Gateway
(DG) represents a space that corresponds to higher delay, but better lifetime than the
optimally placed static sink scenario. Better lifetime in this approach will be expected
because the dynamic selection of DGs helps distribute the gateway energy load among all
the sensor nodes. Slightly higher delay compared to the static sink case can be expected,
because a majority of the time, the location of the mobile sink will not correspond to the

delay-optimal sink location.

 

 

 

 

 

 

 

 

 

       
    
   

 

 

 

 

 

  

 

 

 

Single-hop Routing with
, Sink Visiting Each Node
l Feasible Sowon Space \
(D
g / \ ’ Non-Real-time, Mulehop‘! Scope of
0 Routing with Sink Visiting the
% lnfeasible only gGs thesis
2 ar et ace
‘35 Solution Space \ [T g p ]
t7) / \
.2 \_______/
E ,
U) z: Real-time , Solution Space
‘42, gagwgth Single Dex-i. to be Avoided
fl Per Partition
2, '; .317 5&5 ace. 2:25 \ /
E / Connected Routing Disconnected Routing A
8 105 to 1003 of ms 1003 of Seconds

 

 

Data Collection Latency (Log Scale)

 

 

 

Figure 3-2: Solution mapping to latency and lifetime performance
Note that the space above the real—time monitoring solution is fundamentally infeasible.
This is because any connected routing would require constant sensor-to-sink physical
connectivity and multi-hop routing, and the latter causes enough energy drainage to

prevent the lifetime from being close to the maximum. However, a better Iifetirne can be

30

achieved by the non-real-time solution in which the mobile sink collects data from
multiple designated gateways. Data from each sensor is uploaded to one of those gateway
nodes. Due to the disconnected nature of the routing here, the delay is expected to be
several orders of magnitude larger than the real-time case. Finally, note that the space
below the non-real-time solution should be avoided because in spite of larger delays, the
lifetimes offered by solutions in this area are too short.

Thus, there is a clear collection delay and lifetime tradeoff associated with the data
collection solution space as observed fi'om Figure 3-2. This thesis focuses on the
non-real-time solution with the sink visiting a subset of DGs for collecting data.
Non-real-time data delivery is appropriate for a number of monitoring type applications
[117]. In such applications, a number of considerations besides lifetime and energy come
into play for sink path determination. For instance, for a sink that periodically visits a
network deployed in hostile terrain for data collection, the round trip time essentially
determines the exposure time. In the case of energy constrained mobile devices such as
UAVs [118] which have a limit on their rminterrupted flying time, the trajectory round
trip time has to be planned accordingly. Alternatively, for a sink that collects making
back-to-back trips, round trip time impacts the delay incurred by data packets as
explained in Chapter 6. Thus, given the requirements of a monitoring application,
constraints imposed by the mobile sink (including speed, energy resources and so on), the
data generation rate of the network and such other parameters, an appropriate data
collection solution has to be chosen; single-hop data collection or multi-hop data
collection using mobile sink or a static sink. Thus, it is important to devise a generalized

framework that encompasses the static sink scenario, single-hop data collection at the

31

extremes and all multi-hop solutions in between them. In this thesis, the non-real-time

mobile sink data collection problem has been formulated in a generalized way. In Chapter

6, a simple model has been developed based on key network, mobile sink and application

parameters and analyzed to show the energy-delay balance achieved by the framework.

3.2.2

Distributed Algorithms/Protocols for Generalized Framework

Based on the survey of the literature presented in Section 2.4.2, it can be seen that

mobile-sink-based solutions can be realized with one of the following trajectory

computation mechanisms:

1.

3.

Centralized: A centralized entity that maintains sensors’ location information
computes the trajectory and programs the sink accordingly. In addition to poor fault
tolerance, the primary disadvantage of this approach is that the trajectory
adaptations due to topological changes can be very slow.

Random-Walk based: Sink trajectory consists of a series of random steps and at
every step, the sink randomly picks a direction and distance to travel. While
traveling, the sink collects data from the nodes that come in direct contact.
Random-walk-based mechanisms can also be designed without localization
services.

Area Scanning: Given the dimensions of the sensor field, the sink computes a
trajectory to scan the entire sensor field area. The trajectory depends on the
dimensions of the sensor field and the radio communication range. This approach

does not necessarily require any sensor localization services, either.

In this thesis, the sink trajectory computation mechanism developed to realize the

proposed generalized collection framework is based on a different approach. Sensor

32

nodes cooperatively and dynamically determine the sink trajectory based on the physical
network topology. The nodes themselves guide a mobile sink along the computed
trajectory in a firlly distributed manner. Trajectory adaptation in this approach can be
more reactive than the centralized case. The proposed distributed framework called
Network Assisted Data Collection (NADC), is described in Chapter 7. Network-guided
navigation mechanisms are designed without relying on localization. The developed
distributed algorithms/protocols are implemented in simulation and evaluated. The
performance evaluation is presented in Chapter 8.

In Chapter 9, the model first developed in Chapter 6 is further analyzed from the
perspective of stability of data collection. In addition, simulation results that validate the
model results are also presented. In Chapter 10, the model is extended to take the
capacity of the MDH-DG links into account and the impact on stability is explored. The
insights gained in Chapter 9 and Chapter 10 are applied to a heterogeneous data rate
sensor network and the distributed protocol framework (N ADC) presented in Chapter 7 is
enhanced. The enhancements designed for NADC and its experimental evaluation are
presented in Chapter 11. The contributions of this thesis are summarized and ideas for

future work are outlined in Chapter 12.

33

Chapter 4. Collaborative Node Mobility for Energy Conservation

4.1 Introduction and Related Work

The node mobility framework presented in this chapter is aimed at networks which
consist of mobile sensors sending data to a static sink as introduced in Section 3.1. In this
case, the sensor nodes themselves (e. g. robotic sensors) can be mobile to some extent.
The idea is that the sensor nodes execute a controlled and concerted mobility sequence
for extending network life by distributing routing energy overhead across the network.
The only other solution in the literature that proposes such a mobile-sensor-based
approach reduces energy consumption by adjusting [84, 85] the positions of the
intermediate nodes on a per-route basis from each sensor to the base station as discussed
in Section 2.4.1. Their key idea is to move intermediate nodes on a route such that they
are evenly spaced on the line between the source and the destination. Such a node
placement has been proven to minimize transmission costs incurred by nodes when
power control is used. While the main emphasis of this mechanism is to extend network
lifetime by reducing the energy spent per delivered data packet, we employ a different
approach. The main emphasis of our mobility control framework is to achieve energy
load distribution without altering the packet transmission cost. In this new approach,
energy consrunption across the sensor nodes is distributed by executing a cooperative
mobility mechanism based on localized energy information at the nodes, while the
base—stations remain static. The goal is to extend network life without incurring
additional delivery latencies (unlike the energy constrained routing mechanisms, which

give rise to longer paths), especially in networks with limited path diversity.

34

4.2 Biologically Inspired Problem Formulation

The proposed controlled node mobility in this thesis has been inspired by a natural
cooperative behavior that was observed in Emperor Penguins [119] living in the
Antarctica. During the winters, a group of emperor penguins form a creche, which is an
aggregation of penguins huddled together in close proximity to improve the collective
heat insulation of the group. A creche consists of an inner portion and an outer periphery.
The penguins in the periphery are exposed to the brutal winds and frigid temperatures and
hence, lose more heat than the birds near the center. During the course of the winter,
penguins on the periphery move into the central regions and the creche appears to move
en-masse fi'om its formation spot. As a result of this, the total heat loss of the group is
thought to be evenly distributed across all the individuals. The creche is a way of
providing warmth and ensuring that the group survives the winter. The penguins appear
to take turns to be at the periphery and as a result no individual penguin is exposed to the
fiigid temperatures for too long at a stretch. This behavior conserves body heat for the
entire colony and enables most penguins to make it through the winter.

The idea of our work is to develop a parallel between the penguin creche system and
mobile sensor networks in which data is sourced from the nodes and is destined to one or
multiple base stations, as shown in Figure 2-1:a. For such network models, nodes near the
base stations are exposed to higher energy loss due to aggregation and higher routing
overhead. Energy drainage rates for the nodes in a multi-hop network are shown in Figure
2-l:b. Observe that the nodes close to the base-station demonstrate significantly higher
energy consumption rates compared to the nodes that are away from the base station. The

nodes close to base stations can be compared with the penguins that have to withstand

35

higher body heat loss at the periphery of their creche. Similarly, nodes far away from the
base station enjoy low energy drainage rates similar to the penguins in the inner portion
of the creche.

The goal of this work is to deve10p c00perative node mobility algorithms with similar
objectives of the mobility behavior of Emperor penguins for evenly distributing the
routing energy load across sensor nodes for extending network life. This requires that
sensor nodes have mobility, and the resulting load distribution should be able to extend
lifetime even after compensating for the energy for the physical node movement itself.
This can be particularly challenging when considering the fact that energy cost for
physical movement of a sensor node can be several orders of magnitude higher than the
packet communication cost. Using simulation with the energy budgets of a realistic
sensor and the movement energy of a micro-robot system prototype [120] we
demonstrate that the proposed controlled mobility can work exceptionally well in a
number of application scenarios. The localized mobility mechanism developed in this
thesis is called Energy Aware Swapping Protocol (EASP) which is executed by the
mobile sensors for determining their mobility pattern to extend the overall network life.

The proposed mechanism is general in not assuming that the base stations are
necessarily located along the periphery of a network, as shown in Figure 2-1. For any
arbitrary base station placement, there will be routing hot-spots around the base stations.
The goal of the derived movement in EASP is to ensure that nodes spend comparable
amounts of time in the hot-spots for even energy loss distribution. By distributing the
routing energy consumption evenly, EASP is able to extend the network life.

The main contributions of this work are as follows. First, we show that there is a

36

parallel between the heat insulation problem in the penguin colony and uneven energy
drainage problem in sensor networks. Second, inspired by the goals of the penguin
survival strategy, we propose a mobility algorithm to elongate lifetime in networks where
nodes are capable of limited mobility. Penguin mobility and the proposed EASP
algorithm share a common goal — namely, enhancing the system lifetime by ensuring that
all entities are exposed to severe conditions for equal durations. However, the actual
mobility algorithm in EASP is developed in accordance with the constraints of sensor
networks -namely, the relatively high cost of mobility, routing, and MAC overhead
incurred due to movements. Third, we have developed a localized distributed protocol to
support EASP mobility. Finally, we have evaluated the performance of EASP on linear
and two-dimensional networks and compared the performance with existing solutions.
In the simulation experiments, the cost of mobility has also been taken into account.

This chapter is organized as follows. Section 4.3 uses an example to motivate the role
of controlled mobility in sensor networks. Sections 4.6 thru 4.9 describe the proposed
EASP algorithm in detail and present analysis for linear sensor networks. Chapter 5

presents the performance evaluation of the EASP fiamework.

4.3 Controlled Mobility for Energy Load Distribution
4.3.1 Example Scenario
A. Shortest Path Routing (SPR)

We consider sensor-to-base station data model that holds in most networks with
monitoring style applications [117, 121]. For such a data model, the simplest way to
minimize per-packet energy consumption is to use shortest path routing fiom each node

to its destination base station. For example, in Figure 4-1:a, the shortest path routes

37

from nodes A and B to the base-station are through nodes B and E respectively. Assuming
nodes A and B as packet generators, and equal initial energy across all nodes, E will be
the first node to be energy-exhausted, in which case the network will get disconnected
fiom the base station. Additionally, while node B is burdened with routing A’s data, nodes
C and D remain unused. This example illustrates as to how while minimizing the cost per
packet, the shortest path routing can create hot-spots (node B in this case) due to the lack
of load distribution.

B. Energy-aware Routing (EAR)

 

AO

 

(b)

 

 

 

 

Figure 4-1: Benefits of location swap for extending network longevity

Now consider the situation when an energy-aware routing scheme [44] is used in the
above network. If the B to base station route is established first, it is likely to be routed on
the shortest path through E. But then, for node A to base station, the route ACDE will be
chosen. Note that although this will lessen the energy burden on node B (compared to the
shortest path case), due to the increased hop-count it will increase the total amount of
energy that is spent to route packets generated by node A. Also, since node E continues to
be the bottleneck (as it is the only node connected to the base-station), it will still be the

first node to be energy-exhausted, and to cause a total disconnection from the base station.

38

This example demonstrates that for certain topologies working with a many-to-one data
aggregation model, energy aware routing does not always help to extend the network life.
C. Controlled Mobility

Now consider if the nodes were capable of moving, then as node E is running out of
energy, nodes D and E could swap their positions. After such a swap, if nodes A and B
both continue to use the shortest path route, then the route becomes ABD as shown in
Figure 4-l:b. This ensures that the energy expended per packet from node A to the base
station remains the minimum. Also, since the hop count remains the same as before, the
swap does not affect the delivery latency. Most importantly, the position swap ensures
that the lifetime of network connectivity with the base station is extended compared to
the shortest path routing and the energy-aware routing cases. This is the basic strategy

adopted in EASP.

4.4 Network and Application Model

We assume a sensor field to be configured into equal size tiles as shown in Figure 4-5.
One mobile sensor is deployed per tile for sensing desired parameters from the area of
that tile. The deployed node can be located anywhere in the tile, and moves around within
the tile for monitoring purposes. We assume monitoring style applications [117, 122] that
require each node to generate data at a constant rate and to send it to a base station that is
located within or at the periphery of the field. There could be multiple base stations, but
data from a node is always sent to only one base station. The tiled network model
assumed for this work is an application abstraction and is not a requirement for the EASP
protocol framework itself. EASP can be extended to arbitrary network topologies with

appropriate protocol extensions.

39

The wireless transmission range with respect to tile dimension is assumed to be large
enough so that the nodes in two adjacent tiles can always communicate with each other
irrespective of their individual locations within the respective tiles. This requires two
nodes, placed in the diagonally opposite comers of two adjacent tiles, to be able to
communicate. The number of neighbor nodes depends on the shape and size of the tiles.
For example, in the network in Figure 2-1, each node has a maximum of eight neighbors,
one in each of its adjoining tiles.

Nodes are mobile, and according to our EASP protocol a node can swap position with
any of its neighbors. The goal is to develop a conflict-free algorithm for an energy-aware
node swap pattern that can extend network life by evenly distributing the routing load

across the network.

4.5 Problem Formulation
Consider a network with n tiles with a node in each tile routing data to the closest of the

available base stations. Each node has an initial energyE , and generates data at a

ini
constant rate and routes the data to the base station using shortest path routes. The energy

aware node mobility problem can be formulated as finding the sequence of locations (tile)

or a series of tuples (timehtilei) for each node such that the time until the death of the

first node (T) is maximized subject to the constraint that each tile should have a node at

all times.

4.6 Energy Aware Swap Protocol (EASP)
4.6.1 EASP Architecture and Protocol Outline

The architectural components of the EASP framework are depicted in Figure 4-2.

40

 

 

[ Application Layer

 

 

Energy Aware Swap Protocol Framework

 

 

Routing
Layer

 

 

 

 

 

 

I Energy Aware Sensor MAC

 

 

 

 

Figure 4-2: EASP protocol modules

4.6.1.1 Neighbor Discovery

Using a standard Hello protocol, each node gathers and maintains the a) Residual
Energy, b) Energy Drainage Rate (EDR), and c) Neighborhood Energy Drainage Rate
(NEDR) information about each of its l-hop neighbors. EDR is defined as the combined
communication and swap energy spent per unit time, which is measured by each node
druing its sojourn time at a given location. NEDR for a node is computed by averaging
the gathered EDR values from all its l-hop neighbors. In other words, NEDR represents
the energy drainage rate information about the neighborhood of the l-hop nodes of the
node receiving the Hello packets. Therefore, NEDR contains information about nodes in

the 2-hop neighborhood of the node under consideration.

4.6.1.2 Swap Evaluation and Partner Selection

Based on the above information gathered about its l-hop and the 2-hop neighbors, each

node periodically and asynchronously executes an algorithm that evaluates its own and its

41

neighbors’ residual energy and the energy drainage rates. Based on its relative state
with respect to its neighbors, the node decides whether it is beneficial to swap, and if yes,
it selects one of its neighbors as the swap partner. If a node decides not to swap at the
current time, then it stays at its current location until it re-evaluates the situation at the

next swap evaluation phase.

4.6.1.3 Swap Initiation and Conflict Resolution

If a node decides to swap with a selected partner, then it initiates a physical swap
operation with the partner through a conflict-free swap process. A swap conflict can occur
if two nodes select the same neighbor to swap with at the same time. To avoid such
conflicts, EASP introduces a mechanism that ensures that only one node can swap with a
given node at any given time. A physical swap occurs only after a node initiates a swap

with a selected partner and the partner agrees to do so.

4.6.1.4 Physical Swap with Route Handoff

Once a swap partner is selected and any conflicts are resolved, the node and its swap
partner physically move to each other’s locations, referred to as the target-destinations.
When two nodes swap, they are also required to mutually handoff their routing table
information in order to make sure that the connections that were previously routed
through them continue to function after the swap. The physical swap operation involves
the following steps:

a. The swapping nodes compute a handoff location and move towards it.
b. Upon reaching the handoff location, they execute a handoff process

c. The nodes then move from the handoff location to their target-destination

42

locations.

4.6.2 Interaction with the Core Protocols

MAC Layer: The EASP architecture does not make any specific assumptions about
the underlying MAC layer. However, a collision-free and energy-efficient MAC is
expected to leverage the benefits of EASP to its fullest extent. Scheduled TDMA access
control protocols [123, 124] are known to provide the best energy performance due to
their near-zero collisions and zero idling energy expenditure [6, 37] achieved through
synchronous interface on-off operations [125]. However, as used for our experiments in
Chapter 5, random access protocols [126] such as ALOHA, CSMA or CSMA/CA with
reduced idling energy expenditure can also be used with the EASP protocol framework.

Routing Layer: The operation of EASP is also de-coupled from the specifics of the
routing layer. Usual shortest path, geographic [127-129], or energy-aware routing
algorithms [130, 131] can be used. Data-centric routing protocols such Directed Diffusion

[132, 133] can also be used.

4.7 Protocol Details
The algorithmic details about the EASP protocol module are described in this section.
4.7.1 Swap Evaluation and Partner Selection
The goal of this component is to periodically evaluate if the node would benefit from
swapping with one of its neighbors. In addition, the objective is to select the most
beneficial swap partner from among the neighbors during the evaluation process. The key
idea used here is to achieve the global objective of network life elongation by making
each node to prolong its own life by local cooperation with its neighbors. We attempt to

achieve this using a cooperative strategy inspired by the Emperor Penguins. A desirable

43

cooperative strategy for the penguins is that every penguin in the creche periodically
attempts to swap its position with a neighbor penguin based on the available localized
information such as the temperature and the penguin’s own energy state. The collective
goal of survival of the entire population could then be achieved as a result of this highly
localized swap process. In the proposed EASP framework, the mobile sensors use a very
similar localized and cooperative swap approach that is based on the sensors’ own energy
and the energy status of their neighbors.

The primary criterion used by a sensor in EASP is to move to a position that has a
lower energy drainage rate than its current position. Therefore, each node tries to swap its
position with a neighbor that is in a better energy draining position than its own.
However, the physical mobility of a swap operation can be fairly energy-intensive, and
therefore, the number of swap operations needs to be minimized. In order to control the
number of swaps, it is essential that a secondary criterion should also be a part of the
swap decision. Based on two different secondary criteria for controlling the number of
swaps, we propose two flavors of EASP: EASP-Residual Energy (EASP-RE) and
EASP-Lifetime (EASP-LT). EASP-RE and EASP_LT both require neighbors’ energy
drainage rate (EDR) and the residual energy information that is periodically gathered
using the neighbor discovery protocol as outlined in Section 4.6.1.]. A sensor node is
assumed to be able to estimate its residual energy by tracking its battery voltage drop
with time as outlined in [134]. Based on this overall logic, the basic swap evaluation
process is designed as follows:

1. During periodic evaluation of its neighbors’ EDRs, a node identifies the neighbors

with EDR values lower than itself as potential swap partners.

44

2. The node then evaluates the secondary condition which is different for EASP-RE
and EASP-LT.

a. In EASP-RE, the node verifies to see if any of the potential swap partners

have a residual energy that is greater than its own by a threshold value

referred to as Th

swap -
b. In EASP-LT, the node assesses lifetime gain that can be achieved by
swapping with each of the potential partners. The lifetime gain computation
takes into account the energy cost of the swap operation itself. First, the
expected lifetime of the node and its potential swap partners at their current
locations is computed as the current residual energy divided by the EDR of
their respective locations. Since we define the time until the first node
death as lifetime, the minimum of the expected lifetimes of the node and its
potential swap partners is considered to be the local lifetime, and referred

to as the Current_ life. Next, for each of the potential swap partners,

assuming that the two nodes will swap with each other, the expected
lifetime of the node and its potential swap partner is calculated after
subtracting the energy cost of the swap from their residual energy values.
The minimum of the two expected lifetimes is the post-swap lifetime, and

is referred to as lifetime_p0st_swap. Now, the secondary criterion is
satisfied if lifetime_post_swap is greater than the Current_life by the

threshold value Th

swap ‘
3. Finally, of all the potential swap partners that satisfy the primary and the secondary

criteria, the node chooses the lowest EDR neighbor. If none of the neighbors

45

satisfy both conditions, then the node decides not to initiate a swap at the current
time. Note that this does not preclude the node from performing a swap if the swap
operation is initiated by one of its neighbors.

The swap evaluation and partner selection logic is summarized in the pseudo code
shown in Figure 4-3. Two extended versions of the swap algorithm EASP-RE-ZH and
EASP-LT-ZH are also proposed in order to study the performance improvement of EASP
by extending its EDR and other information scope from only l-hop neighborhood to up
to 2-hop neighborhood. In EASP-RE-ZH, for example, each node not only maintains the
EDR values of its l-hop neighbors, but also the average EDR of all its l-hop neighbors’
neighbors. This quantity is called the Neighborhood EDR (N EDR), which is computed by
each node and exchanged as a part of the Neighbor Discovery process. Apart from the
primary and secondary criteria described above, a third criterion needs to be satisfied for
the swap decision in these 2-hop versions of the EASP protocols. This third condition is
that the EDR of the evaluating node must be greater than the NEDR of the evaluated
neighbor as a potential swapping partner.

The idea behind this third condition is that before initiating a swap with a neighbor, a
node should make sure that there are no nodes around that neighbor that might benefit
more from swapping with this neighbor. It ensures that all nodes allow more beneficial
swaps to happen, whenever possible. Experimental results described in Section 5 .2
indicate that adding the 2-hop EDR information to the swap evaluation process indeed

improves the performance of the EASP framework.

46

 

 

Swap Evaluation and Partner Selectigr at Node i:
”initialization
edr_dfi_seen_sojar <— 0
swap _partner <— NULL // no swap partner selected yet

edr_difl<— EDR(i) — EDR( neighbor) //compute difi'erence in EDR values

// check if this is the lowest EDR neighbor seen so far

0

l

2

3

4

5 for (each neighbor in Neighbor_Table) {

6

7

8

9 1f ( EDR(neighbor) < EDR(i) and (edr_difl_seen_so_far < edr_difl") ) {

10 If (secondary_criteri0n is satisfied) { //see below

11 If (EDR( i) > NEDR(neighb0r)) // required only for 2H0p version of EASP
12 swap _partner <— neighbor

13 }

14 }

1 5 }

16 If (swap _partner was selected)

17 Initiate swap operation

1 8

19 Secondary Criterion Check:

20 EASP-RE: if (remaining_energy(neighbor) > remaining_energy(i)+ Thmap)
21 Secondary criterion satisfied

22

23 EASP-LT: //c0mpute current lifetime of node i and neighbor

24 Lifetime,- «— remaining_energy(i) / edr(i)

25 Lifetimeneighbo, <— remaining_energy(neighbor) / edr(neighb0r)
26 Current_life +— minflifetimei, lifetimeneighbor)
27

28 // compute estimated lifetime gain post-swap

29 lifetime _psi <— (remaining_energy(i) — swap_cost) / edr(neighb0r)
30 lifetime aneighbor <— (remaining_energy(neighbor) — swap_cost) / edr(i)
31 lifetime _post_swap +— min(lifetime_ps,~, lifetime aneighbor)

32

33 If (lifetime _post_swap > current_life + T hswap)

34 Secondary criterion satisfied

35

 

Figure 4—3: Pseudo code for swap evaluation and partner selection

47

 

4.7.2 Swap Initiation and Conflict Resolution

 

 

 

 

 

 

 

 

Figure 4-4: Timing diagram for swap initiation and conflict resolution

Since the sensors perform autonomous swap evaluation asynchronously, it is possible
that a node is simultaneously selected as a target swap partner by more than one of its
neighbors. To deal with these conflicts, EASP introduces a conflict resolution technique
similar to the RTS/CTS mechanism used in the IEEE 802.11 MAC protocol. Two
messages, namely, Swap-Reg and Swap—Proceed are used for beginning the swap

operation. Upon deciding on a swap, a node sends a Swap-Req packet to the selected

48

partner. If the partner is available for a swap, then it responds with a Swap-Proceed
packet. This way, the neighbors of the two swapping nodes become aware of the swap in

progress and hence any further contentions are avoided.

 

 

 

> Handoff Points

 

 

 

 

 

 

 

 

Figure 4-5: Straight and diagonal handoffs

Figure 4-4 shows the timing diagram for an example swap operation between nodes A
and B (in Figure 4-5), initiated by A after it selects B as its swapping partner. Node A
initiates a swap by sending a Swap-Reg message to Node B. In the mean time, node P
which is not aware of the Swap-Request from A, also sends a Swap-Request to sensor B.
Since the request from A was received first, node B ignores the Swap-Request from P.
Then node B responds to the Swap-Reg from node A with a Swap-Proceed message.
Sensor P waits for the Swap-Proceed and eventually times out. It checks to see if it can
swap with one of its other neighbors and finds that node N satisfies the necessary swap
conditions. So, it initiates a swap with node N. This demonstrates that multiple

simultaneous swaps in the same neighborhood are possible as long as each node is

49

involved in at most one swap operation.

After the handoff phase is completed, both A and B continue broadcasting Hello
messages to establish new neighborhood relationship with the nodes around. The new
neighbors of A namely P, Q, N and R add node A to their neighbor list. Note that nodes P
and Q have to add a new entry for node A while nodes N, R and B have to only change
the status information. Similarly, when node B sends out a Hello message, nodes R and N
change the status of B. After the swap, B is no longer a neighbor of P and Q. Hence,
nodes P and Q eventually time out and remove entry for node B from their neighbor
tables. This concludes the localized swap initiation and conflict resolution algorithm.
4.7.3 Physical Swap with Handoff

The physical swaps operates through the following three distinct phases.
Phase 1: Move towards a computed handoff point

Depending on the relative locations of the swapping sensors the swaps could be
‘Straight’ or ‘Diagonal’. For example, swaps between nodes A and R or between A and B
in Figure 4-5 will result in a ‘Straight’ swap, whereas a swap between A and N will be
‘Diagonal’. This swap type determines the location of the handoflpoint, at which the
routing table and any other relevant information are mutually handed off between the
swapping partners. In the case of a straight swap, the handoff location is computed to be
the intersection point of the line joining the current locations of the two sensors and the
tile boundary line between them. For a diagonal swap, the handoff location is the comer
point at the intersection of the two tiles in which the nodes are located. The handoff
points for each kind of swap operation are shown in Figure 4-5. It is important that a

swapping node does not abandon the routing responsibilities of its pre-swap tile before its

50

swap partner is ready to take over the routing responsibilities of the tile. To ensure this,
the handoff is performed when both the swapping partners reach the computed handoff
point. In a non-tiled network with arbitrary topology, the handoff point would be selected
midway between the locations of the two swapping partners.
Phase 2: Perform route handoff

Upon reaching the handoff point, the node that had initiated the swap operation,
initiates route handoff by sending a Handofl—Init message. The swap partner responds
with a HandofiT-Cmplt message. The nodes exchange information about their old locations
with each other such as tile identifier, next hop and the EDR values. After this point, the
swapping nodes take over the routing responsibilities of the new tile.
Phase 3: Move to the target destination

During this phase, the swap partners move to the desired position within the target tile,
and continue to send out periodic neighbor discovery messages indicating their
ownership of the new tile. Nodes that were routing packets through the swapping sensors
update their next hop information to reflect the swap operation when. they receive these
messages from the swapped nodes during this phase. Consider the example scenario in
Figure 4-5, in which node Q was routing packets through node B before a swap between
A and B took place. After the handoff between A and B is completed, based on A’s Hello

packets, node Q updates its next hop to A and starts routing packets through it.

4.7.3.1 State Machine for the Physical Swap

Figure 4-6 and Figure 4-7 show the state machines for the swapper (initiating node)
and the swappee during a physical swap. The five states and their associated events and

actions are described as follows:

51

IDLE: This is the default state of a sensor node. It can transition to the PRE-MOVE
state as a swapper after it sends a Swap-Reg message to initiate a swap. As a swappee,
a node can enter in the PRE-MOVE state when it receives a Swap-Reg message.

PRE-MOVE: Upon receiving the Swap-Proceed, a swapper moves to the MOVE
state. If it does not receive the Swap-Proceed within a certain duration, it goes back to

IDLE. A swappee enters the MOVE state after it sends a Swap-Proceed message.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

s—wm Send Swap-Request
IDLE
Decided to swap
Reached final with neighbor
location
Tlmer expiry
PHY-MOVE PRE-MOVE
CMPLT Move towards Wait for
Final location Swap-Proceed
Received
Received Swap-Proceed
Handofi-Cmplt
Reached handoff
HANDOFF location MOVE
Tlmer Send Handoff-Init. Move towards
expiry Wait for Handoff-Cmplt Handoff location

 

 

 

 

 

 

 

 

Figure 4-6: State machine for the swap operation at Swapper

MOVE: Both the swapper and the swappee remain in this state while moving
towards the handoff location. Upon receiving the handoff location, the nodes enter the
HANDOFF state.

HANDOFF: The nodes remain in this state while executing the routing information

52

handoff, and exit to the PHY-MOVE-CMPLT state only after the swappee sends the
Handofl-Cmplt message and the swapper receives the HandofiCCmplt message from
the swappee.

PHY-MOVE-CMPLT: In this state, both the swapper and the swappee move towards

their desired location in the new tile, after which nodes reenter the default IDLE state.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

wa e Periodically perform
swap decision
IDLE
Received
Reached final Swap-Request
location
Timer expiry
PHY-MOVE PRE-MOVE
CMPLT Move towards Send
Final location Swap-Proceed
Received
HANDOFF Handoff-lnit MOVE
Send Handoff-Cmplt Move towards
Handoff location

 

 

 

 

 

 

Figure 47: State machine for the swap operation at Swappee

4.8 Design Generalization for Arbitrary Topologies

To be applicable for arbitrary non-tiled topologies, the EASP framework will need to
be extended for accommodating the following aspects. First, the distance between
neighbors can be larger than the tiled model, since the neighborhood will no longer be

restricted by the tile boundaries. Second, when a swap occurs, the swapping nodes may

53

not be within the communication range of all the neighbors of the swapping partner
immediately after the handoff. The temporary lack of communication between a node and
its new neighbors can lead to a temporary disruption of the route and cause some data
loss. To deal with the data loss, the swap evaluation and partner selection algorithm
should be enhanced to take the distance between the swapping nodes into account. Finally,
there will not be any restrictions on the number of neighbors a node can have. This means
that a node can have more than eight swap partner choices. Note that most of these
extensions involve implementation issues, while the basic concept of EASP remains the

831116.

4.9 Analysis of Swaps in Linear Networks

4.9.1 Performance Upper Bound

 

 

 

 

 

 

N1 N2/ Sensors N3 Nn-1 N"
I O i Q i O ------- l ----------- i -O i 0
L1 L2 L3 Ln 1
" Locations "

 

 

Figure 4-8: A linear network with n nodes and one base station

For analytical tractability, we do this analysis for a linear network of n nodes as shown
in Figure 4-8. Also, we assume a synchronous version of EASP in which swap
evaluations happen at the same time periodically on all nodes. Since the amount of time
that a node stays at a location (also referred to as the sojourn time) is expected to be much
larger than the physical time for a swap, physical swaps are assumed to occur
instantaneously. Each node periodically generates a certain amount of data destined to the
base-station. Data packets are forwarded in the direction of the base station by each node.

Thus, each location has an EDR value proportional to the number of packets that are

54

generated and forwarded by it. All nodes have the same initial energy and the swap cost is
assumed to be zero (only for this analysis). Lifetime of the network is defined as the time
at which the first node in the network runs out of energy.

The maximum lifetime of the network can be achieved when all nodes get energy
exhausted exactly at the same time. In other words, the target will be to attain a situation
where the sum of the energy left on all nodes in the network is zero. With zero swap cost,

the maximum achievable lifetime can be written as:

n
Tmax = (Eim't X") ZEDRi (1)

i=1
where n is the number of nodes, E im-t is the initial node energy, and EDRi is the
energy drainage rate at location i. EDR,- is higher for locations nearer to the base

station because of their higher routing burden. Now, to achieve maximum lifetime, all
nodes have to equally share the routing burden. Therefore, since each location has a
unique energy drainage rate, every node must spend equal amount of time in each
location in the network. Assuming negligible swap durations compared to the sensor
sojourn time, the total time that a node should spend at each location (sojourn time) can

be expressed as:

T sojourn __time = T max / n
This implies that every node has to swap in such a way that it visits every location in the
network at least once. Because of the unavailability of any circular path in the linear
network, each node has to traverse to one end and then back to the other end to ensure
that it visits every location. This implies that a node actually needs to visit each location

twice. As a result, the modified sojourn time of a node at each location (this is also the

55

interval between two consecutive swap cycles) can be written as:

n
Swap _Interval = Tmax /2n = Emit/2 x ZEDRi (2)
i=1

where Tmax is the maximum lifetime as computed in equation (1). Now, let the

minimum number of swaps required to achieve the maximum lifetime be Min swaps .

 

 

 

 

 

 

 

 

 

 

 

 

N1 N2 N3

Tum/6 e e o ‘
N1 N3 N2

W. o o o A
N3 N1 N2

Tm” O o o A
N3 N2 N1
2Tm/3 O O 0
N2 N3 N1
T N2 N1 N3

"‘3“ o o o A

 

 

 

Figure 4-9: Swap sequence for maximum network life in a 3 node linear network
Qase_l: If n is odd, the number of nodes involved in a swap during a swap cycle is (n-I).
With two nodes per swap, the number of swaps is (n — l) / 2 . If the interval between swap
cycles is Swap_Interval , the number of swap cycles that can occur within Tmax is one
less than Tmx /Swap __Interval , which is (Zn-1). Therefore, the minimum number of

swaps required to achieve the maximum lifetime:

Minswaps = (2n - l)(n — l)/2.

56

 

 

 

 

 

 

 

 

 

Tum/6 A

Tm/s A

Tam/2 A
N1

2Tma/3 O O A” O A

srma/e A

Tm A

 

 

 

 

Figure 4-10: Trajectory of N2 and N3 during the maximum life swap sequence

Figure 4—9 shows the swaps that are required and the intervals at which these swaps
must occur in a 3 node network in order to achieve the maximum lifetime. For this
network, 5 swap cycles are required at intervals of Tmax /6. Figure 4-10 shows the
movement pattern of the center node and an end node in the 3-node linear network.
Note that, being in the center, node N2 has to move to one end of the network and then to

the other in order to visit all the locations.

Case2: If n is even (n > 2), using the similar logic as above, the minimum required swaps

for maximum life can be derived as: Min swaps = n(2n — 3)/2 .

Lifetime with non-zero swap cost: When the zero swap cost assumption is relaxed,
the maximum lifetime is expected to be relatively lower. However, for a given It, the

minimum number of swaps for the maximum life is expected to remain the same. This

57

is because the required swap pattern still needs to ensure that the nodes spend equal
amount of time at each location. The pattern does not depend on the initial energy or the
swap cost itself. Therefore, the maximum lifetime (best case) with non-zero swap cost
can be obtained by deducting the cost of the required number of swaps from the initial

energy in equation (1) resulting in:

n
T = (E. . xn)— Min XS x2 2 EDR.
max zmt swaps cost 1' _1 1
where Scost is the cost incurred by each node involved in a physical swap.

4.10 Summary

In this chapter, a cooperative node mobility framework called EASP was developed to
evenly distribute the routing energy burden experienced by nodes in networks with
many-to-one data model. Inspired by principles of penguin colony strategy for surviving
frigid winters, a swapping mobility algorithm for nodes was proposed. The protocol
components and distributed algorithms required for the swapping decision and the
handoff mechanisms based on localized information were developed. The performance
upper bound for EASP was derived for linear networks. Performance evaluation of

EASP is presented in Chapter 5.

58

Chapter 5. Energy Aware Swapping Protocol - Performance
Evaluation

In Chapter 4, Energy Aware Swapping Protocol (EASP) was developed and the various
components were presented. In this chapter, the performance results obtained from
simulation experiments of EASP are presented. Two simulators were used for the
evaluation: a C-based simulator and N82 based detailed packet level simulation. The run
time required to run lifetime simulations using the detailed N82 implementation was
impractical. Therefore, we used the detailed simulation to run EASP using lower initial
energy for nodes and analyzed the viability of the EASP protocol despite the control
overhead and packet losses during handoff and MAC effects. The C simulator was used
to run the simulations with large nodal energy to evaluate the long-term performance of
EASP and to compare it with a centrally computed best solution.

While the results from both implementations are very useful for performance
evaluation, the results obtained from these two implementations for similar scenarios are
not directly comparable. This is because the C simulator does not model the MAC and
the physical layers. Thus, the MAC layer retransmissions and losses are not taken into
account. Also, it cannot run the swap conflict resolution and handoff functionality
associated with state machines specified in Figure 4-6 and Figure 4-7.

Lifetime of a network is defined [87] as the duration after which at least one node in
the network becomes energy exhausted. In most scenarios, including in this linear
network, it has been observed that the energy exhaustion first happens to a node that is
visiting a location l-hop away fi'om the base station, thus rendering the network
disconnected from the base station. Due to this, observing the first instance of energy

exhaustion does provide a practical perspective of the network life. Also, in several of our

59

experiments, the CPU time taken for simulating the energy exhaustion of the entire
network, especially with all the EASP protocol components, proved to be prohibitively
long. Because of these two reasons we have chosen to adopt the first node exhaustion

definition of network life.

5.1 Results from C Based Simulator
The preliminary performance evaluation of EASP was conducted using a simulator
written in C. Simulation experiments were run on a tiled sensor field with one sensor

node in each tile as shown in Figure 2-1. Based on a sensor transmission range of 30m,

the size of the tile was chosen to be lllelm. For a simulated data monitoring
application, each sensor node generates a 512 byte packet every second, destined for the
base-station. We modeled the mobile sensor nodes as a MICA MOTE [34] sensors
attached to LEGO Mindstorm robotic vehicles [120]. The Mindstorm robots are powered
by two Lego Technic 43362 motors [135]. Specifications of the sensor MOTE and the

vehicle motors are shown in Table l.

 

Sensor Mote Related Parameters

 

 

 

 

 

 

 

 

 

 

 

 

Transmission range lOOfi or 30.5m
Transmit Current 12mA
Receive Current 1.8mA
Data rate 40kbps
Motor related parameters

Rotation speed 219 rpm

Torque 2.25N.cm
Electrical power 1 .1W

 

 

Table 1. Sensor node and vehicle motor specifications
The transmission cost in MICA for a 512 byte packet (based on the values shown in

Table l) is 3.8 mJoules and the cost of receiving a packet is about 0.6 mJoules.

60

Communication cost, the energy consumed to transmit and receive one 512 byte packet,
is therefore 4.4 mJoules.

Swap cost is defined as the amount of energy consumed by a node to physically move
across tiles. Assuming a swap distance of 11m (tile size), and considering the rpm and
power of the LEGO motors as shown in Table 1, the energy required to drive the LEGO
motors for a swap is 40.8 Joules. Since two nodes are involved in a swap, the total cost of
each swap is 81.6 Joules.

Performance of the distributed EASP, as proposed in Section 4.6, has been evaluated
and compared with the following protocols:
SE Shortest path routing is used to route packets to the base-station. No node
swapping is executed.
Centralized Swapging Algorithm (CSA): An algorithm that relies on global network
topology and initial energy information performs swaps that result in maximum operating
lifetime for the linear network as described in Section 4.9. The performance of this
algorithm is considered to be the upper bound for the performance of EASP.
Energy-Aware-Routing (EAR): We have implemented a constrained routing algorithm
[44] which maximizes the minimum remaining energy along any route from a sensor
node to the base station.

Note that the behavior of EAR is expected to be not much better than the SPR case for
networks with insufficient path diversities. Since in our linear network model all packets
have to pass through a single gateway node (see Figure 4-8), there is no path diversity for
EAR to gain over the SPR case. That is why we have compared EASP with EAR only for

two-dimensional networks, and not for the linear network.

61

5.1.1 Linear Topology

Figure 5-1 shows the network lifetime for the topology in Figure 4—8. Lifetime of a
network is defined up to the time at which the first node becomes energy exhausted. The
first observation in Figure 5-1 is that for all protocols, the network life reduces with
increasing network size. This is simply because with a given initial energy, with larger
number of nodes more traffic is generated. With this increased energy demand for the

routing shortens the network life.

 

 

50
45 - +SPR
40 - +EASP
35 ~ +CSA

 

 

 

 

30*
25*
20"
151
10*

5i

Lifetime (days)

 

 

 

 

 

Number of nodes

 

Figure 5-1: Network life versus network size

The second observation is that our proposed distributed EASP can significantly
increase the network life over the SPR case. The gain in network life ranges from 55%
for a network size of 64 nodes to 78% for a network size of 32.

The last observation is that EASP performs nearly as well as the centralized protocol
with global knowledge of network topology and initial energy information. This indicates

that the distributed swap algorithm with local information, as executed by the Emperor

penguins [119] can deliver near best case performance as outlined in Section 4.9.

To understand the protocol dynamics better, we plot (in Figure 5-2) the standard
deviation of nodal energy across the network at different points in time. For the SPR case,
this quantity increases monotonically simply because each node, depending on its

location (and routing burden), drains energy at a different rate.

 

 

 

25
ISPR
20_ IEASP
ICSA

 

 

 

     

 

 

Standard deviation of remainging energy
(kJoules)
8

11.57 23.15 34.72 46.30 48.72

 

 

Time (days)

 

Figure 5-2: Spread of nodal energy with time

 

 

 

 

 

Number of nodes SPR EASP CSA
8 0.01908 0.0203 0.0192
16 0.0365 0.0395 0.0371
32 0.0715 0.0773 0.0738
64 0.141 0.148 0.1509

 

 

 

 

 

 

Table 2: Energy per packet (Joule)

An interesting observation in Figure 5-2 is that although CSA provides the longest

63

network life, its remaining energy variation is more than that of EASP at intermediate
points in time. This can be explained by the fact that CSA performs swapping based on
the global knowledge of the topology and the initial energy distribution. The goal for is it
to derive a swap pattern (as explained for a three node network in Section 4.9.1) for
lifetime maximization. It does not take intermediate energy conditions in to account and
therefore no part of the protocol attempts to reduce the variance of energy across nodes
during the intermediate points of operation. EASP, on the other hand, works based on the
temporal energy distribution and explicitly attempts to minimize the variance by
executing swaps. This accounts for smaller standard deviation numbers of EASP in
Figure 5-2.

Average energy required to deliver each packet for all three protocols is reported in
Table 2. This includes the energy required to transmit (3.8 mJoules), receive (0.57

mJoules), and perform node swap, which is 40.8 Joules.

 

 

 

 

 

 

 

 

 

 

 

 

7
6.5 i’\‘\'\‘
6 _
4
”>’, 5.5 -
03
E
m 5 -
.§
2&3 4.5 - 'SPR
-‘ +EASP
4 - +CSA
3.5 L r . 1
3 . I . .
0 4000 8000 12000 16000
Swap Cost/Communication Cost ratio

 

Figure 5-3: Impact of swap cost on network life

64

Note that the protocols that consume lower energy per packet do not necessarily
provide longest network life. SPR, for example, has the lowest energy-per—packet because
it does not have any swap cost. The network life for this protocol, however, is the shortest
(see Figure 5-1).

Both EASP and the centralized algorithm have higher energy-per-packet costs because
of their swap energy expenditure. In spite of this small increase in the energy-per-packet
figures, both these protocols can deliver longer network life. Results in Table 2 also
demonstrate that although each swap operation is four orders of magnitude more energy
expensive compared to the packet communication cost, EASP, just like the centralized

algorithm, is able to limit the number of swaps, so that there is an overall energy gain that

 

 

 

 

 

 

 

 

 

 

 

 

 

extends the network life.
50 4
45 ‘ //
0
r 40-
Iii 35 . +SPR
>
E} 30 _ +EASP
o +CENT
0, 25 .
.E
.E 20 s
(U
E, 15 -
8 1o -
9
2 5 .
<
o\° 0 i . 3
O 10
Number of nodes

 

 

 

Figure 5-4: Average remaining energy at the end of network life
The impact of swap energy cost on the longevity of a network is shown in Figure 5-3.

For a given communication cost, as the swap cost increases, the gain in lifetime gets

65

offset by the swap energy consumption, and the network lifetime drops. It is notable that
despite the relatively large cost of the physical swaps, our proposed EASP algorithm is
able to significantly extend network life by successfully distributing the routing load.

Figure 5-4 shows the average amount of energy left in the nodes as a percentage of
initial energy, with increasing network size. Large values of remaining energy for the
SPR protocol indicate that when the network life ended because of energy exhaustion of
at least one node, plenty of energy is still available in the network because of the poor
energy distribution achieved by this protocol. Observe that for smaller networks, the
remaining energy numbers are very close to zero for both EASP and the CSA protocol.
With increasing network size, although the remaining energy remains more or less
constant for the CSA protocol, it increases for EASP. This indicates that there is still
room for improvement in the distributed EASP mechanism.

5.1.2 Two-dimensional Topology

 

 

 

 

 

 

 

 

 

 

 

250 -
200 -
’u?
a
3 150 -
O.)
.E
‘5
=5 100 -
50 —
0 - - . j
10 30 50 7O 90 110
Nu mber of Nodes

 

 

Figure 5-5: Network life versus network size

66

The distributed EASP algorithm, proposed in Section 4.6, has been also implemented
for the two-dimensional networks as shown in Figure 2-1. Experiments reported in this
section were carried out on networks with one base station that is connected to three
sensor nodes which act as the gateway nodes. As for the data model, each sensor node
periodically generates fixed size packet and sends it to the base station.

Performance of EASP (with shortest path routing) has been compared with SPR (with
shortest path routing), and the constrained Energy Aware Routing (EAR), as described in
Section 5.1. From the results in Figure 5-5, it is evident that like in linear networks,
EASP can deliver better network life compared to both EAR and SPR protocols.
Network life reduces with network size due to increased overall traffic load, as observed
for the linear case in Figure 5-1.

Note that the EAR routing did not have any significant lifetime extension over the
SPR protocol. The reason for this is two—fold. First, since EAR is not constrained to
shortest paths, it was found to be taking longer routes compared to the SPR protocol. As a
result, although the energy load was somewhat distributed by EAR, its energy-per-packet
numbers looked worse than those for SPR. Higher energy per packet offsets part of the
gain due to energy load distribution. The second and more important reason is that for
nodes near the base station, not much energy distribution can be achieved using EAR. For
example, the three gateway nodes will have to carry the entire routing burden irrespective
of the load distribution achieved by EAR in the rest of the network. When these gateway
nodes become energy-exhausted, the network becomes useless. For EASP, on the other
hand, these three gateway nodes are not fixed. In other words, when a gateway node

becomes somewhat energy depleted it simply swaps with a neighbor with better energy

67

condition. In fact, a large number of nodes serve the role of energy-demanding gateway
nodes at different points in time. This provides excellent energy load distribution across
nodes, and the result is network life extension by a factor of 2 to 3.5.

Figure 5-6 shows the number of nodes alive at any given time for all the protocols
under consideration. So far, we have defined lifetime as the time until the death of the
first node. Another reasonable definition of network life would be the duration for which
a certain percentage of the nodes remain alive, or the network remains connected with the
base stations. To understand such behavior better, we conducted experiments on an 8x8
network with two base-stations and five gateways until all the gateway nodes to the

base-station were energy-exhausted.

 

 

 

 

 

 

 

 

 

100 i
99 '
98 '
a) 97 ‘
g
g 96 -
OJ
13, 95 -
z +SPR
o\° 94 ' +EAR
93 ' +EASP
92 l l I
0 50 100 150
Time (days)

 

 

 

Figure 5-6: Remaining alive nodes with time

Results show that with SPR protocol, the first gateway node dies after approximately
20 days, which is earlier than EAR (40 days) and much earlier than EASP (150 days).

After all 5 gateway nodes are energy—exhausted, the sensor network is considered to be

68

disconnected from the base station. Therefore, in Figure 5-6, network disconnection time
for a given protocol can be indicated by the time up to which 92% (59 non-gateway
nodes out of 64 nodes) of the nodes are alive. For SPR, EAR and EASP, such
disconnection times are found to be 60.2 days, 59.8 days and 150 days. With network
disconnection time as the metric, the performances of SPR and EAR are almost identical,

but EASP outperforms both of them by approximately two and a half folds.

5.2 Detailed Packet Level N82 Simulation

Using the N82 network simulator, EASP experiments were run on a tiled sensor field
with one mobile sensor per tile as shown in Figure 4-5. All EASP modules (described in
Section 4.6 and Figure 4-2) including the EASP Neighbor Discovery, Swap Evaluation
and Partner Selection, Swap Initiation and Conflict Resolution, and the Physical Swap

with Route Handoff were implemented within NS2. Sensor transmission range and the

tile dimension were chosen to be 30m and 1071le respectively. These dimensions
ensure that the nodes in two adjacent tiles can always communicate irrespective of their
individual locations within their respective tiles. As for the data model in the baseline
case, each sensor generates a 512-byte packet every second, destined for the base-station.
The effects of packet size variation on EASP performance are reported later in this
section.

IEEE 802.11 [136, 137] is used as the medium access control (MAC) protocol. In
order to emulate energy-aware sensor MAC protocols [123, 124] with zero idling, carrier
sensing and overbearing energy expenditure, we set those expenditures to be zero for our
employed version of the 802.11. As the communication cost, we account for the energy

drainage incurred only by the transmissions and receptions.

69

As for the mobile sensors, we have modeled the experimental parameters based on a
Swarm Capable Autonomous Vehicle (SCAV) [138] prototype that was designed around a
MICA2 MOTE [34] sensor and a LEGO Mindstorm robotic vehicle [120]. The
Mindstorm robots are powered by two Lego Technic 43362 motors [135]. The motors
rotate with a speed of 219 rpm while producing a torque of 2.25N.cm. Assuming an
average swap distance of 10m (tile size), the energy required to drive the motors for a
swap operation is 37.1 Joules. Since two mobile sensors are involved in a swap, the total
cost of each swap operation is 74.2 Joules. An initial energy of 81 KJoules (corresponds
to two AA batteries used by the MICA2) was assigned for each sensor node.

5.2.1 Evaluated Protocols

The following protocols have been evaluated for performance comparisons.

Shortest Path Routing (SPR): During the network initialization, two minimum spanning
trees, each rooted at each base-station, are created. Each node forwards its packets
through the tree that provides a shorter hop-count to the tree root. No swaps are executed
in this protocol.

EmAware-Routing (EAR): We have implemented a constrained-routing [44]
algorithm which maximizes the minimum remaining energy along any route from a
sensor to its destination base station. Like in the SPR protocol, spanning trees are created
such that the tree for each base station keeps track of the minimum energy node that has
been encountered along a branch of the tree. Each sensor picks a next hop node such that
it maximizes the minimum residual energy along the path to the base-station.

The EAR protocol is generally able to perform better than the SPR when there is

sufficient route diversity in the network. Since no such diversity is present in linear

70

networks with single base station (see Figure 4-8), we have not evaluated EAR for those
networks; it has been evaluated only for two-dimensional networks.

Energy Aware Swap Protocol (EASPk The EASP protocol, as described in Section 4.6,
was implemented with the parameterization as outlined in Section 5.2. Data routing for
these experiments are always performed using the shortest paths. As explained in Section
4.7.1, a node executing the EASP-RE version of the protocol requires the residual energy

of its swap partner to be higher than its own by a percentage threshold value (Th swap ).

This implies that as the nodes approach the end of life stage, they satisfy the swap
threshold requirement more easily and tend to swap more frequently. This can nullify the
gain in lifetime achieved during the earlier stages.

In order to prevent frequent swaps at the final stages, we ensure that nodes only swap
when there is enough life left to recover the energy cost of the swap operations. We refer
to this End-ofLife-Limit. Note that such a limit is not required for the EASP-LT version
of the implementation as it already takes energy cost of swap into account in its
secondary condition as mentioned in Section 4.7.1.

5.2.2 Performance Results

5.2.2.1 Benefits of Swap in Linear Topologies

Figure 5—7 shows the lifetime for the linear network in Figure 4-8 with varying number
of nodes.

The first observation in is that for all protocols, the Figure 5-7 network life reduces
with increasing network size. This is simply because with a given initial energy per node,
with larger number of nodes more traffic is generated. With increased energy demand for

routing, the network life is shortened. The second observation is that the distributed

7]

EASP protocols can significantly increase the network life over the SPR protocol. The

gain in network life ranges from 74% for a network size of 8 nodes to almost 80% for a

 

 

 

 

 

 

network size of 32.
12
10 .
2‘? 8 .
C
i
3 6 - EASP-LT-2H
Q)
.E
f? 4 1
3
2 _ SPR
EASP-RE-1 H ——/
O I I I I I
5 1O 15 2O 25 30 35
Number of nodes

 

 

 

Figure 5-7: Lifetime for different protocols in linear topology

We have reported the performance of EASP-RE-lH and EASP-LT-ZH, which are the
best performing EASP protocols in their respective categories with l-hop and 2-hop
energy and EDR information. The lifetime gain achieved by EASP-RE-IH and
EASP-LT-ZH are very similar in small networks. However in larger networks, while
EASP-LT-ZH is able to sustain the gain, the EASP-RE-IH protocol offers only
marginally better network life compared to that of SPR. With EASP, elongation of
network life fundamentally depends on swaps for periodically replacing energy-drained
nodes close to the base station by energy rich nodes away fi'om the base station. As the
network size increases, the absolute life of the network decreases, thus for a given initial

energy the number of swap opportunities also decreases. With fewer swap opportunities

72

available, the quality of the swap becomes more crucial for large networks. With 2-hop
energy and EDR information and taking the swap cost into account while performing
swap evaluations, the EASP-LT-2H protocol is able to perform better quality swaps
compared to its EASP-RE-IH counterpart with lesser amount of information. This
explains why EASP-LT-2H outperforms EASP-RE-IH in large networks.

To summarize, the results in Figure 5-7 indicate that the proposed biologically inspired
energy aware swap strategies do elongate the network life for one-dimensional sensor

networks.

5.2.2.2 Swap in Two-dimensional Topologies

In addition to SPR and EAR, we compare the performance of EASP with a centralized
implementation of MAPC (Mobility Assisted Power Control) solution [84, 85, 139], in
which the core idea is to move the intermediate nodes on a route in such a way that they
are evenly placed along the line from source to destination as mentioned earlier in
Section 2.4.1. With transmission power control, such a configuration has been shown to
minimize communication cost [84]. In essence, MAPC attempts to reduce the EDR
values associated with nodes using controlled mobility. EASP, on the other hand
distributes the energy drainage more evenly across nodes. Note that the MAPC solution is
independent of the underlying routing strategy. Therefore, MAPC with shortest path
routing implies data is routed to the base-stations along routes with similar hop counts as
in SPR. In other words, the packet delivery ratio, average hop count, and delay should
remain the same as in SPR. The metrics that are affected by the intermediate node
alignment and the resulting transmission power optimization are: 1) energy cost incurred

per packet, and thereby 2) network lifetime. In this sub-section, we show MAPC results

73

where they are different from SPR. It is important to note that the presented results of
MAPC reflect its best performance by using load-balanced shortest hop routes through a
centralized implementation. Also note that transmission power control is used only in
MAPC protocol, whereas for SPR, EAR and EASP protocols the maximum transmission

power is used.

 

 

18
16 i

 
   
 

EASP-LT-ZH

14- 14

12‘

  

EASP-RE-lH

—D
o
1

Lifetime (days)
co

 

 

 

6..
4...
2- m . s
0 I I T l r I I

 

 

 

Number of nodes

 

Figure 5-8: Network lifetime of 2-dimensional networks

Network‘Life: Experiments on two-dimensional tiled networks (see Figure 4—5) were
carried out with two base stations, where each base station is within the transmission
range of two neighboring tiles. As a result, four mobile sensors act as gateways between
the sensor field and the base stations. As for the data model, each sensor periodically
generates fixed size packets destined to the closest (shortest hop) base station.
Performance comparison between SPR, Energy Aware Routing (EAR) [44], MAPC[84,

85], and EASP protocols is presented in Figure 5-8. Note that EASP, MAPC, and SPR

74

work with sensor-to-base station shortest path routing, whereas EAR chooses energy
constrained routes based on the mechanism described in Section 5.2.1. In Figure 5-8, it is
evident that like in linear networks, EASP can deliver better network life compared to
both EAR and SPR protocols. Network life reduces with increase in network size due to
increased overall energy load as observed for the linear case in Figure 5-7.

Observe that the EAR routing extends network life by a small amount over the SPR
protocol as it balances the load across the gateway nodes. However, the relative gain is
much smaller than that of the EASP protocols. There are two reasons for this. First, since
EAR is not constrained to shortest paths, it was found to be taking longer routes
compared to the SPR protocol. As a result, although the energy load was somewhat
distributed by EAR, its energy-per-packet numbers (shown in Table 3) are worse than
those for the SPR case. Higher energy-per-packet expenditure offsets part of its gain due
to the achieved energy load distribution. Note that the longer routes also increase the
packet delivery latency as discussed later in this section.

The second and more important reason is that for nodes near the base stations, not
much energy distribution can be achieved using EAR. The four gateway nodes for two
base stations will have to carry the entire routing burden irrespective of the load
distribution achieved by EAR in the rest of the network. When these gateway nodes
become energy-exhausted, the network becomes disconnected. For EASP, on the other
hand, when a gateway node becomes somewhat energy depleted it simply swaps with a
neighbor with better energy condition. In fact, a large number of nodes serve the role of
the energy-demanding gateway nodes at different points in time. This provides excellent

energy distribution across nodes, and that results in network life extension by a factor of

75

3.25 to 8. Due to the same set of reasons outlined in Section 5.1.1 for the linear networks,

the EASP-LT-ZH protocol outperforms EASP-RE-IH.

 

 

  
 
 
 
 
 

1400 — L a SPR DEAR IMAPC I EASP-RE-IH I EASP—LT-ZH j
1200 - 5
Network death for SPR /Network death for EAR
1000 -
Network death for MAPC

Network death for
EASP-RE- l H

    

 

 

 

 

Std. Deviation of Residual Energy

 

 

 

 

2.89 3.47 4.63 5.79 6.94 9.2611.5712.7312.85

Time (days)

 

 

 

Figure 5-9: Residual energy variance in a 8x8 network

In Figure 5-8, it can be observed that MAPC performs better than SPR by a factor of
2.8 to 4.2. By reducing the packet transmission energy on the nodes along a route, and
balancing the load across the gateway nodes, the gateway nodes in MAPC are able to
survive longer compared to SPR. The lifetime improvement seen by MAPC over SPR is
higher than what has been reported in [84] and [85] primarily due to our centralized
implementation. Even so, EASP consistently performs better than MAPC suggesting that
EASP achieves better energy load distribution which is substantiated by the residual
energy variation across nodes, as shown in Figure 5-9.

Residual Energ: In Figure 5-9, we report the standard deviation of the remaining nodal
energy with time. For the SPR protocol, this quantity increases monotonically because

the nodes on the routing hot-spots becomes increasingly more energy depleted compared

76

to the other nodes, and thus the variance in the residual nodal energy increases. For the
EAR protocol, similar trend of increasing standard deviation can be also observed. This is
due to the fact that the energy depletion rates are variable over nodes depending on their
routing burden, and constant over time due to the absence of any swaps. As a result the
relative difference in residual energy across the nodes monotonically increases over time.
MAPC also shows the same variance trend as SPR and EAR. However, the variance is at
a smaller scale because of the lower energy consumed per unit time by nodes. Also, note
that the final standard deviation values reached by EAR and MAPC around network

death are the same. MAPC takes longer to reach that value.

 

 

100
SPR

90- + a
n #‘9
80‘ % l\#;___’_____,—I

 

 

 

§

// \

g 60 ' '\ MAPC

1% 5° ‘ EAR

E -

g 40 EASP-LT-ZH
.\°

 

 

 

 

30 40 50 6O 7O 80 90 1 00 1 10

 

 

Number of nodes

 

Figure 5-10: Average remaining energy at the end of network life

The EASPs, on the other hand, are able to maintain relatively smaller standard
deviation due to their swap operations. Towards the end of the network life, both the
EASP protocols experience slightly increasing standard deviation due to reducing swap
rates caused by the end-oflife-limit effect as explained in Section 4.7.1. This decrease in

77

the swap rate is caused by the fact that there is not enough lifetime left in the network to
recover the swap energy cost following each swap.

. Figure 5-10 reports the average remaining energy after the network life expiry, as a
percentage of the initial energy. Large remaining energy for the SPR, MAPC, and EAR
protocols indicate that plenty of energy remains available in the network when at least
one node becomes energy exhausted. These undesirable effects are due to the lack of
energy distribution in SPR and MAPC, and long routes in EAR. Although, SPR and
MAPC use shortest path routes, MAPC is able to use more energy in the network because
routes are more load-balanced. Also, although MAPC outlives EAR, remaining energy is
lower for EAR primarily because EAR uses longer routes and hence incurs higher energy
cost for delivering packets. For the EASP protocols, however, the remaining energy
numbers are relatively much smaller. For example, in a 6x6 (36 nodes) network, only
3.7% of the nodal energy remains after the event of the first nodal energy exhaustion.
With increasing network size, the remaining energy for EASP-LT-2H slightly increases.
This slight performance degradation is caused due to fewer swap opportunities as a result
of shortened network life for larger networks.

Swap Counts: Figure 5-11 shows the swap count per node during the defined network
life. As anticipated, the protocol EASP-LT-ZH, equipped with up to 2-hop energy and
EDR information and taking the estimated life time information for the swap decisions,
swaps much less aggressively, but more judiciously, compared to its l-hop counterpart.
This trend is valid for all network sizes, although the number of swaps turns out to be
insensitive to the network size itself. Very little additional room for swap reduction for

this protocol appears to be the reason for this.

78

 

900

 

800 -
700 ~
600 ~
500 -
400 -
300 -
200 -
100 -

Swap cou nt per node

 

 

36

   

49

Nu mber of n odes

EASP-RE-1 H

64

EASP—LT—ZH

  

 

81

 

 

Figure 5-11: Per node swap count for EASP protocols

For the 1-hop EASP-RE-IH protocol, however, the swap count reduces monotonically

with increasing network size. Due to higher network traffic and energy load, and the

subsequent reduction in network life accounts for this trend of swap frequency reduction.

 

 

 

 

 

 

 

 

 

 

 

Network size SPR EAR MAPC lEASP—RE-IH EASP-LT-ZH
36 16.9 23.2 6.24 31.55 18.9
49 19.3 25.75 7.82 32.6 21.3
64 21.7 28.8 8.64 31.6 24.4
81 24 34.9 10.44 33 27.25
100 27 36.3 11.15 38.5 28.95

 

 

 

Table 3: Energy per packet (in milliJoules)

Energy ger Packet: The average energy required to deliver each data packet is reported

in Table 3. The energy per packet includes expenditures for transmission (3.4 mJoules for

maximum power), receptions (2.06 mJoules for maximum power), and for the physical

79

node swaps, which is 37.1 Joules for the swapping nodes. Note that the protocols with
lower energy-per-packet do not necessarily provide longest network life. The MAPC and
SRP protocols, for example, have relatively lower energy per packet values because of
min-hop routes and zero swap costs. MAPC has lowest energy per packet value on
account of its node placement strategy and power control for reducing communication
cost. For the EASP protocols, the huge energy expenditure incurred by the physical swap
operations and the conflict resolution protocol as outlined in Section 4.7.2 increases the
per packet energy expenditures. However, their longer network life allows much higher
data delivery, thus the energy cost incurred for swaps is spread over a larger number of
packets. This offsets the swap energy costs and brings the energy per packet costs down
to the vicinity of that for the SRP protocol. With smaller number of swaps (see Figure
5-11) compared to the EASP-RE-IH, the energy-per-packet figures for the EASP-LT-2H
are very close to those for the SRP protocol. But interestingly, in spite of their higher
energy per packet numbers, both the swap based protocols deliver longer network life as
reported in Figure 5-8. Finally, the energy aware routing protocol (EAR) has the highest
energy per packet cost because of its longer routes. Additionally, because the number of
packets delivered with EAR is lower than the EASP protocols, and therefore, the cost of
maintaining energy aware paths is spread over a smaller number of packets which leads
to higher energy cost per packet. As pointed out earlier, MAPC extends lifetime by using
controlled mobility to achieve lower EDRs, while EASP reduces the variance in nodal
energy consumption. Note that it is possible for EASP and MAPC to be employed
simultaneously in which case higher lifetime can be expected than the individual

strategies alone.

80

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6
65 ‘
5.5 ~
EAR 5 _ EAR 9
55 -
’3
g g 4.5 '
E q
; 45 8 4 _
‘—“ a
Q)
o I _
35 _ 3.5
l\ 3 _ +SPR
+EAR
25 - +SPR 2 5 _ +EASP-RE-1H
"‘ EAR ' + EAS P-LT-2H
+EASP-RE-1H
1 5 + EASP-LT-2H 2 . . . . .
30 45 60 75 90 105 3° 45 6° 75 9° 105
(a) Numberofnodes (b) Numberofnodes
Figure 5-12: a) End-to-end delay and b) route length
100 = = = ‘
lEASP-LT—2H 77"
100 a EASP-RE-1H 99 ' SPR
I EAR 98 — EASP
0’ 8 _
g 81 E 97
5 E 96 ~
2 g EAR
g 64 :8 95 ‘
§ 3 94 ~
2 8
49 n. 93 —
°\°
92 - +SPR
35 91 _ +EAR
+EASP-RE~1H
l I I I 90 f fir I I I
1 1.05 1.1 1.15 1.2 30 45 60 75 90 105
(a) Control overhead (%) (b) Number of nodes

 

 

 

 

 

Figure 5-13: a) Control overhead, and b) Packet delivery ratio
Packet Delivegy Delay: The end—to-end delay performance of the EASP protocols is

shown in Figure 5-12za. EAR demonstrates the highest delay because of its longest

81

routes as evidenced in Figure 5-122b. Since with both EASP-RE-lH and EASP-LT-ZH
packets are always delivered along the shortest path, their delay and route lengths are
same as those for the best case SPR protocol. Note that the route handoff part of the
EASP framework, as described in Section 4.6.1.4, ensures that in spite of the physical
swap operations, the data routes to base stations never change, and therefore the shortest
paths are maintained throughout the network life.

Protocol Control Overhead: Figure 5-13:a shows the percentage control overhead
which is defined as the number of generated control packets normalized by the total
network traffic including the data packets. For EAR, the overhead is contributed mainly
by the neighbor discovery packets with residual energy information used for constrained
route discovery [44]. The EASPs have two sources of control overhead. First, similar to
EAR, they also incur neighbor discovery with EDR and residual energy information.
Second, they use the swap and handoff related control packets as described in Section
4.7.3.

Results in Figure 5-13:a indicates that compared to the EAR protocol, the EASPs have
smaller control overhead, which is in the vicinity of on 1% or so, and that is in spite of
the additional control overhead of the swap-related protocols. This is because the
additional swap control overhead gets distributed due to the much higher number of
delivered packets with the EASPs, and that keeps the percentage overhead low. Also, the
control overhead of EASP-LT-2H is lower than EASP-RE-lH for a) its longer life and b)
fewer swaps as observed in Figure 5-11.

Results in Figure 5-l3zb demonstrates that compared to the EAR protocol, the EASPs

offer better packet delivery rates similar to that of the shortest path routing protocols.

82

As the network becomes larger, the delivery rates for SPR and EASP show a decline due
to mild congestion-related packet drops. In EAR, longer routes incur more transmissions
which give rise to congestions earlier than the SPR and EASP protocols. This leads to the
lower packet deliveries for EAR even for smaller networks.

Based on the observed results so far, it can be concluded that the 2-hop EASP-LT-ZH
protocol consistently outperforms its l-hop EASP-RE-IH counterpart, and therefore the

next set of results will include only EASP-LT-2H as the best case EASP performance.

 

 

850
800 - 9x9

750 2

70° ‘ 8x8
650 -

 

600 r

% Lifetime gain over SPR

550 6x6

 

 

500 I I I I I I T
5 6 7 8 9 10 11 12

Swap Th resh old (%)

 

 

 

Figure 5-14: Impact of swap threshold on EASP performance

Sensitivity to Swap Thresholds: Figure 5-14 shows the threshold (77: swap ) sensitivity
of the EASP-LT—ZH protocol. With very small Thswap values, the protocol was observed

to swap very aggressively, and the lifetime benefits of the swaps were seen to have been

offset by the hefty energy costs of the physical sensor moves. With very large Th swap , on

the other hand, sluggish swaps were observed to defeat the basic goals of the EASP

83

framework. It was experimentally found that the EASP-LT-2H protocol is relatively

insensitive to 771 for a reasonably large window of the threshold — that is

swap
approximately from 5% to 12% for a wide range of network sizes. This result can be used

as a guideline for swap threshold selection for the EASP process.

 

 

30

25-

EASP-LT—ZH

201

Lifetime (days)
a

 

 

 

 

 

 

 

10 1
5 .
O . . . . .
0 200 400 600 800 1 000 1 200

 

 

Packet Size (bytes)

 

Figure 5-15: Impact of data packet size on performance

Impacts of Packet Size: Figure 5-15 shows the impacts of varying packet size on the

lifetime in an 8X8 (64 sensors) network. With a fixed packet rate, increasing packet size
increases the overall energy load and, as demonstrated in Figure 5-15, reduces the
absolute overall network life. The relative gain of EASP-LT-ZH over SPR, however,
remains quite high across the entire range of experimented packet sizes. The relative gain
ranges from approximately 700% for the 128 byte packets to 450% for 1024 byte packets.
To summarize, although the absolute network life depends on the packet size, the lifetime

advantage of EASP varies from about 7-fold improvement for smaller packets to over

84

4.5-fold improvement for larger packets. These results indicate the suitability of the
EASP swap framework for a wide range of target sensing and similar applications with

different packet size requirements.

 

 

10 -- EASP-LT—2H
6__ SPR EAR

4 _.. ¢

0 - t i

0.0E+00 6.8E+03 1.4E+04 2.7E+04

Lifetime (days)

 

 

 

 

 

Swap/Communication Cost Ratio

 

Figure 5-16: Impact of swap cost to communication cost ratio

Sensitivi to Relative Swa and Communication Costs: Swap experiments were

 

conducted on the 8X8 topology while varying the energy cost of a physical swap
operation from zero to larger values up to four orders of magnitude higher than the
communication energy cost. The communication cost was kept constant during these
experiments. Results in Figure 5-16 indicate that increasing the swap cost with respect to
the communication cost leads to lower lifetime gain. For instance, the lifetime shrinks
from 14 days with zero swap cost to around 12 days with the highest cost used.
However, the notable result is that even with up to four orders of magnitude relative swap
costs, the EASP system is able to deliver significant amount of lifetime elongation
compared to the plain SPR and EAR routing mechanisms. It turned out that the

85

consideration of the swap cost in EASP-LT-ZH’S expected lifetime computation during

the swap decision process is responsible for its desirable insensitivity to such large swap

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

costs.
14 100 I
l +SPR
12 ' +EAR
90 -
+EASP-LT—2H o
”a; 10 ' a:
(U
as l—
3 8 80 -
w .2
.E F)
e s
:1 3‘5 70 h
8
°\., +SPR
60 - +EAR
+EASP-LT—2H '
0 T r l r 50 . I , r
0 2 4 6 8 1O 0 2 4 6 8 10
(a) % packeterror (b) °/o packet error

 

 

Figure 5-17: Error Sensitivity a) Lifetime, and b) Delivery ratio

Sensitivity to Packet Errors: To study the impact of channel errors on the performance
of EASP, we conducted experiments on the 8X8 topology with packet error rates in the
range of O to 10%. The error in these experiments represents the error seen above the
MAC layer after the re-transmission attempts failed. Results in Figure 5-17:a indicate that
the lifetime for SPR and EAR increase marginally as the error rate increases. This is
mainly due to the fact that the lost data packets, caused by channel errors, do not have to
be routed all the way to the base station. This results in routing energy savings - resulting
in higher lifetime, though at a cost of reduced packet deliveries as seen in Figure 5-17:b.

However, in case of EASP-LT-ZH, the presence of error adversely impacts the swap

86

protocol (see Section 4.7) - resulting in fewer swaps. The higher the error, the fewer the
swaps and hence the lower the lifetime. These results underscore the need for an
error-free channel or sufficient protection at the link layer to ensure the delivery of swap

control packets to get significant lifetime gain with the EASP paradigm.

5.3 Summary and Conclusions

In Chapter 4 and Chapter 5, a mechanism for extending network life by introducing a
biologically inspired energy-aware mobility in wireless sensor networks has been
presented. The concept has been inspired by a natural grouping behavior of Emperor
penguins for conserving body heat during the winters. Based on this concept, we provide
a distributed sensor mobility framework that works based on localized energy
information and makes sure that the routing load across sensor nodes in a network is
evenly distributed to collectively extend the network life. A performance upper bound for
linear networks was derived and compared with the performance of the distributed EASP
protocol. Proof of concept and long term lifetime gain of EASP was established using a
simple C-based simulator. Experimentally, using an extensively built simulation model in
NS2, we have evaluated two versions of the proposed framework.

Experimental results demonstrate that using the distributed algorithms for biologically
inspired controlled node mobility, it is possible to significantly extend the network life.
We also show that this holds in situations where the energy cost of node mobility is
modeled as up to four orders of magnitude larger than the communication cost for each

packet.

87

Chapter 6. Generalized Framework for Joint Trajectory and Routing
Optimization

The approach proposed in Chapter 4 is applicable to networks composed of nodes that

are capable of limited mobility. However, a large percentage of sensor network

deployments are expected to be static in which case node mobility based lifetime

elongation techniques do not apply. In this chapter, we consider such static networks and

address the data collection problem using the mobile sink approach as outlined in the

contribution summary presented in Section 3.2.

6.1 Sensor Network Data Collection

Data collection delay and network life are often considered the two most important
issues while designing an embedded sensor network as discussed in Chapter 1. As shown
in Figure 6-1:a, the best delay can be obtained when data from the sensors is uploaded to
a static sink in real-time using multi-hop routes. This approach, however, can suffer from
limited network life due to the traffic hotspots at the nodes around the sink.

The key idea behind mobile sink, also referred to as Mobile Data Harvester (MDH),
is that the nodes around the sink change over time and therefore the energy drainage due
to routing occurs more uniformly, thereby extending the network life. With mobile sinks,
the protocols for packet routing and sink navigation need to be designed based on a clear
tradeoff between the data delivery delay, network lifetime, and the sink exposure time. As
shown in Figure 6-l:c, for applications with large delay tolerance, a mobile sink can
collect data from individual sensors by following a trajectory spanning all the network
nodes. Locally sensed data is buffered at each node until the sink comes in contact.
Therefore, the delay experienced by the data packets is in the same timescale as the round

trip time of the mobile sink. While incurring a large delay, this mode of data collection

88

provides the most energy efficient solution since the transmission energy expenditure is

incurred only for a single hop. Due to its best energy and the worst delay performance,

this single-hop collection can be considered to be the other extreme of the traditional

static sink scenario as illustrated in Figure 6-12a, which offers the best delay and the

worst energy solution.

(3) Static sink: Real—time ( k=kcritical)

(b) Mobile sink (MS): Real-time

 

 

it; “if”

 

 

 

 

(0) MS: Non-real-time sungle-hop (k 1=)

(d) MS: Non-real-time multi-hop (ex. k =2)

 

 

  

 

 

«g

BS

 

 

 

0 Sensor node never in direct contact
with MS.

MS at its current location

 

Node in direct contact with MS at some
point, forwards data immediately to the

Node in direct contact with
BS/MS currently

Designated Gateway (DG) — Comes in
direct contact with MS at some point in
the trajectory, buffers generated or
routed data till contact

 

 

Figure 6-1: Different routing scenarios in static and mobile-sink-based data collection

89

A controlled multi-hop (with a hop bound of k) data collection strategy, as shown in
Figure 6-1:c, can be used for striking a desirable balance somewhere between those two
solution extremes. In this approach, the sink collects data only from a subset of nodes
(referred to as designated gateways or DGs) which aggregate and route data from other
sensors that do not make direct contact with the mobile sink. The aggregated data is
buffered at the DOS until they come in contact with the mobile sink. For a given network,
higher the number of Designated Gateways, lower the overhead of multi-hop routing
(small k) and hence higher the delay and lower the routing energy expenditure. In other

words, by controlling the hop bound k, it is possible to strike a balance between the

packet delivery delay and the energy expenditure. For any given topology, k =1 is

equivalent to single hop data collection and k = k results in the static sink

critical
scenario. The values of k between 1 and keen-ca, can be used for achieving the desired
delay or lifetime performance.
6.2 Application Model and Problem Statement

Sink availability in the sensor field and data collection duration can generally be
represented using a duty cycle model as shown in Figure 6-2. Consider the Mobile Data
Harvester (MDH) in Figure 6-1 :d, that traversing a sensor field to collect data through a
number of pre-selected Designated Gateways. The time required for collecting all data

from the field during each trip is referred to as the trip collection-timet The

0011'
periodicity of the MDH or the duration of the collection cycle isr . The objective is to

minimize ta,” . For stable operation, feel] should be less than or equal tor . The

quantitytcou also represents the flying time or exposure time that a UAV-style mobile

90

sink has to withstand during each collection cycle. With constraints on flying time (or
exposure time) of a UAV (or UGV [96]) sink, the collection framework will have to

incorporate controlled multi-hop routing for shortening the quantity tco ,1.

 

 

 

 

 

tcoll

Sink in the sensor field

 

 

 

 

 

Time

\/

 

 

 

Figure 6-2: Sink periodicity and Data Collection Delay

Delivery delay for a packet is specified as the interval between when it is generated at
a sensor and when it is collected by a sink. Depending on when a packet is generated

within a collection cycle, it can face a delivery delay anywhere between 0 and r , resulting
in an average of T/ 2. The delivery delay does not depend on trip collection-timetcol,
except in applications where r = too” which represents a mobile sink that makes
back-to-back trips to collect data. Note that the collection cycle 2' represents the upper
bound of the collection-timetw” for stable operation. For a given MDH technology such
as UAV or UGV, the collection cycler is assumed to be constant, and it represents the
maximum duration between refueling.

Data collection can be real-time when the incurred delay is primarily in the time-scale

of packet forwarding for applications such as catastrophic event and intrusion detections.

Non-real-time collection, on the other hand, is categorized by delay in the same

91

time-scale of sink mobility, and is applicable for monitoring style applications such as
environmental data monitoring.

While the static sink solution in Figure 6-1:a caters only to real-time applications, both
real-time (Figure 6-1 :b) and non-real-time scenarios (Figure 6-1:c and Figure 6-1 :d) can
be accommodated by the mobile sink approach. From a sink mobility standpoint, the
applications are categorized in Figure 6-3. For all data collection applications,
maximizing the network lifetime is the primary objective. However, depending on the
nature of the application, there might be secondary objectives as shown in Figure 6-3.

Continuous Data Collection (CDC) (1 = tea” ):

 

 

[Mobile Sink based Sensor]

 

 

 

Applications
/ . \ .
Non-real-time i
Continuous Data collection 5

 

Collection (CDC) periodic Data
Collection (PDC)

(Non-real-time)

 

 

 

 

K I. Real-time \

 

 

 

 

 

 

. III. Sink with
Delivery D clay. Same 11' Non-real-time constrained trip time
order of magnitude as Delivery delay =t6011/ Moving or exposure time = icon
statrcsrnkscenarro . .. . Ob" .. . t 5
Obj: Minimize delivery Obi = Mummze tcoll 1’ Mlm‘mze 00”

 

 

clay j \_ Application category
evaluated in the thesis

 

 

 

 

 

Figure 6—3: Characterization of the applications with mobile sinks

The mobile sink is continuously present in the sensor field (1 = ta,” ) collecting

data either in real-time or in non-real-time mode. In the real-time case, as the mobile sink

moves along its trajectory, data from the entire network is routed to the sink at its current

92

location. Conceptually, this situation incurs delivery delay in the same order of magnitude
as the static-sink scenario in Figure 6-1 :a. The objective is to achieve the average delay as
close to that of the static sink scenario.

In the non-real-time case, data is first sent to a set of designated gateway (DG) nodes
using multi-hop routes, and then collected by the mobile sink when it comes in contact
with the DGs as shown in Figure 6-1:b and Figure 6-1 :c. The parameter too], influences
the per-packet data collection delay (between generation at a sensor and upload to the
MDH) and the actual travel time of the MDH in each collection cycle. The goal is to

design sink trajectories and data routes to minimize the average packet delivery delay

[60% (or%).
Periodic Data Collection (PDC) (r > tea” ):
This corresponds to the generalized scenario for tc011> r, in which after each trip

collection-timeta,” the sink exits the sensor field and reenters at the beginning of the

next collection cycle. Since the average delivery delay in this case isy, once the
parameter r is chosen (possibly based on the sink availability), reduction of the quantity

too” does not help from a delivery delay perspective. However, minimizing ta,” by

smart trajectory computation and route selection can offer reduced flying time (or
exposure time) for UAVs and UGVs. For MDH is deployed in a hostile terrain, the

collection time too” also represents the duration for which the mobile sink is exposed.

Therefore, the objective is again to minimize rm”. All discussed application categories are

marked as I through 111 in Figure 6-3.

93

. 6.3 Data Collection Modes
Data collection can take place in two modes namely Collect-on-Move mode or in
Stop-on-Demand mode.

a. Collect-On-Move (COM): In this mode, the MDH moves at a constant speed
throughout its trajectory and collects data fi'om the DGs while it is moving. This
mode of collection is suitable when all data buffered at the DGs can be uploaded
to the MDH within the contact duration. A number of parameters can influence
the suitability of this mode including the data generation rate of the nodes, the
hop-bound k, the speed of the MDH, the transmission range of the nodes. Sink
devices such as fixed winged UAVs [140] [141] that are unable to stop or easily
change their speed should collect data in Collect-on-Move mode.

b. Stop-On—Demand (SOD): In this mode, the MDH moves at a constant speed along
its trajectory and collects data from the DGs as it is moving. However, if it is not
able collecting all the data buffered at the DGs, it stops for the duration required to
collect the remainder of the data. This mode of collection is suitable when the
contact duration is not sufficient to collect buffered data. Sink devices such as
rotary winged UAVs [118]or UGVs [96] that are able to hover or stop can collect
data in this mode.

6.4 Parameterization of Multi-hop Routing

The static, single-hop and various multi-hop data collection scenarios described at the
beginning of this chapter can be parameterized using the unified hop-bound factor k which
represents the maximum number of hops that a packet is allowed to traverse from a sensor

to the MDH. This hop-bound factor essentially determines the extent of multi-hop routing

94

 

in the network. As k increases, average hop count incurred by packets increases. At the
same time, the number of DGs and the hence the trajectory length decreases. As a result,
the amount of data that needs to be uploaded at each DG goes up. When shortest paths are
used for routing, the maximum amount of forwarding that can be experienced by a packet
is the same as the diameter of the network. Therefore, the maximum value for k is the
network diameter D. The minimum value of k that allows static data gathering operation

shown in Figure 6-1:a is termed as ken-tic“, . In other words, ken-“ca, represents the

optimal placement of the static sink with respect to packet hop count. Static gathering is

feasible with multi-hop routing in the range ken-tied, s k s D .

 

Strictly non -real-ti me non-reaI-otl me or
real-time

x
r V—Lm

| I |
l I l

 

 

 

 

 

 

 

1 kcritical D
4— Static Sink —»
e Mobile Sink :
Hop-bound (k) > .

 

Figure 6-4: Feasible region for k with mobile sinks

As shown in Figure 6-4, with a mobile sink, the extent of multi-hop routing can vary

over the entire range of lSkSD. For the sub-range 1 s k < k the data

critical ’
collection is strictly non-real-time since the MDH is not reachable by all nodes at all
points in its trajectory. For the routing range corresponding to ken-”ca, s k s D , both

real-time and non-real-time collections are feasible by the mobile sink. In this thesis, we

95

specifically focus on the region of k (l _<_ k < k arm-ca, ) in which a mobile sink can only

operate in non-real-time mode.

The objective is to develop joint MDH navigation and data routing mechanisms such
that sensor data can be collected fiom a connected sensor network using up to k-hop
transmissions, where k is a tunable parameter. In general, the impacts of increasing k are:

l) Decreasing number of Designated Gateways (see Figure 6-1 :0 and Figure 6-1:d)
2) Increase in multi-hop routing and consequently the energy cost for delivering each
packet
3) Increase in data that is collected from each DG which also increases the time
needed for collecting data fi'om each DG
6.5 Problem Formulation
For multi-hop routing (see Figure 6-1 :d), the hop-bound factor k represents the

maximum number of hops that a packet is allowed to travel while being routed from its

originating sensor node to the mobile sink. For a given topology, k = 1 indicates single
hop data collection with mobile sink, and k = [Cam-ca] indicates the minimum value of
k that allows static data gathering operation shown in Figure 6-1:a. The values of k
between 1 and kcritical (as shown in Figure 6-1 :d) can be used to achieve the desired

delay or lifetime performance. The MDH trajectory computation with the hop-bound

factor k is equivalent to finding a k-constrained minimum spanning trajectory (MST). A

k-constrained MST is defined such that the trajectory is within up to k hop reach of all

network nodes, while providing the minimum possible round-trip distance.

We formulate the k-constrained MST discovery problem as a k -hop dominating set

96

[11] search or a k-hop cluster formation [18] problem. The dominating nodes or the

cluster-heads represent nodes that the MDH has to visit to ensure that data is collected

fiom all network nodes using up to k hop routes. Those dominating or cluster-head
nodes define the mobile sink trajectory and are henceforth referred to as Navigation
Agents. In our formulation, the first hop neighbors of the Navigation Agents act as the

Designated Gateways. Thus, nodes send their data packets to one of the DGs using up to
k—l hop routes. One hop is incurred when the data is uploaded from a D6 to the MDH,

resulting in a maximum of k hops for any packet. In this thesis, we explore and

analyze the above k—constrained MST discovery mechanism and characterize the impacts

of the tunable hop-bound factor k on collection time tea 1,.

 

 

 

 

 

 

 

 

 

Symbol Representation
Network n Total number of nodes in the network
parameters d Node density per unit area (per sq.
meter)
R Radius of the sensor field (meters)
C Capacity of the MDH-sensor link (bits
per second)
Radio r Transmission range of the sensors and
the mobile sink (meters)
Traffic 1 Data generation rate of each node in the
network (bits per second)
Mobile Data v Speed of the mobile sink (meter/sec)
Harvester (MDH)
Policy k The hop-bound factor

 

 

 

 

 

Table 4: Symbols definition

The assumptions made are as follows. First, we assume that no sensor localization

97

services are available to the sensors and the sink. Second, the mobile sinks move at a
constant speed. Third, the link capacity of the sensor-sensor links is sufficiently high so
that the aggregated traffic never exceeds the wireless link capacity. The impact of
relaxing the capacity assumption is considered in Chapter 10. Hence, only the limited
contact time between the DGs and the MDH restricts the amount of data that can be

collected.

6.6 Model for the Hop Bound Factor k

In this section, we develop a model for analyzing the impacts of the hop-bound k on trip
collection-time and energy performance. As mentioned in Section 6.5, the MDH has to
traverse the network such that all nodes are reachable within k hops from the MDH at some
point on its trajectory. Such a problem can be formulated as a k-hop minimum dominating
set problem or k-hop clusterhead determination problem [142] on the graph induced by the
network topology. Such problems are known to be NP-hard and the determination of the
optimal trajectory is beyond the scope of the thesis. Since our focus is specifically on the
impacts of multi-hop routing in the data collection process, for the purpose of analysis, we
consider a circular network terrain as shown in Figure 6-5 with uniform and dense node
distribution. This implies that a MDH trajectory that covers the network area completely
while minimizing the travel distance is required. In the literature, survey trajectories for
mobile robots have been studied to achieve area coverage for remote sensing.
Researchers have found that concentric circular trajectory [143] and spirals [143, 144]
provide firll area coverage with minimum travel distance. In this chapter, for detailed
analysis, we adopt the concentric circular trajectory which is considered one of the best to

cover a region. Specific parameters used for the following analysis are defined in Table 4.

98

 

 
  
 
  
 
 
  
 

Transition """"""""""""""""" Boundary of sensor field

distance Rm ------------------ Sub-trajectory i

Shaded area indicates
the area from which data
is collected when MDH
is on sub-trajectory i

Routing tree rooted at DG

 

Node
0 Designated Gateway (DG)
0 Navigation Agent (NA)
— NHDH Path
------- Boundary of data
collection area

0

   

 

 

 

 

 

Figure 6—5: Sub-trajectories and collection boundaries

In the concentric circular trajectory, the overall MDH trajectory is split into multiple
sub-trajectories of different radii as shown in Figure 6-5. The separation between the
circular sub-trajectories is 63 . Each sub-trajectory is defined by the Navigation Agents
which represent the nodes that have to be visited to achieve area coverage. The distance
separation between Navigation Agents is also set to 65 . The parameter 5 s is chosen to
achieve the degree of coverage that is required. If the value is chosen too low, coverage is
not complete and when chosen too high, the amount of redundant coverage is high. For
our analysis we have chosen to seté's =2kr. The separation distance was chosen to
ensure that the routing regions associated with DGs around each Navigation Agent are
non-overlapping to ensure tractability of the energy analysis presented in Section 6.6.3.

The MDH in this case first completes the outer-most sub-trajectory and then moves

99

inward until it reaches the inner-most one. Between two successive sub-trajectories, the
MDH transitions using the shortest distance path. After reaching the inner-most
sub-traj ectory, it moves back to the outer-most one and starts the next collection cycle.

The MDH moves along its trajectory collecting data from Designated Gateways (DG)
which are chosen to be the l-hop neighbors of the Navigation Agents. All non-DG and
non-Navigation Agent nodes route their data to one of the DOS using up to k-hop routes.
After the routes are set up based on criteria such as shortest-hop or minimized-energy,
each DG maintains a routing tree rooted at itself and spanning across the non-DG and
non-Navigation Agent nodes. Such trees are depicted in Figure 6-5.

6.6.1 Trajectory Length

For the concentric trajectory as shown in Figure 6-5, the overall trajectory is split into

multiple circular sub-traj ectories of different radii. The number of such sub-trajectories

can be written as R/ds where 65 is the separation between the consecutive

sub-trajectories. In our analysis, we use 65 = 2kr . The number of sub—trajectories can be
written as $610,. The radius of the outermost sub-trajectory is R — kr , and adjacent

sub-trajectories are mutually separated by a distance of 2kr. Thus, the radius of the ith
- . . . . _ . _ - = R
sub trajectory (starting from the periphery) rs R [kr + (z 1)2kr] for l 1 to A)”,

and the circumference is 27r[R —(kr(2i —1))]. The total trajectory length L can be

written as:

R/2kr
L = 27: Z [R — (kr(2i —1))]+ 212m (Eq. 6-1)

i=1

where Rm represents the total transition distance (see Figure 6-5) from the outer-most to

100

the inner-most sub-trajectory. The quantity 2Rm represents the cumulative distances

traversed across all sub-trajectories during a complete trip.
6.6.2 Trip Collection-Time

For a given link capacity C, and MDH speed v, with small values of d, A. and k the
contact time for each DG is likely to be sufficient to complete the data upload. However,
for relatively large values of d, k or/i , the MDH may need to stop at all or few DGs. Thus,

depending on the specific parameters, the trip collection-time ta,” is determined either

by the travel time of the MDH, or by the total data upload time - whichever is larger. The

travel time of the MDH can be computed as % . From Eq. 6-1, the move time to

complete all sub-trajectories can be written as:

 

 

R/Zkr 2
27r ;[R—kr(2i—l)]+2Rm [fléh)+2Rm
Tm = "1 v = v ...... (E q. 6-2)

Since the DGs are l-hop neighbors of the Navigation Agents, a set of DGs can be seen
as a cluster around a Navigation Agent. As It becomes larger, fewer Navigation Agents are
required and hence, they get placed farther apart. This also increases the physical
distances between the DG clusters. This implies that on certain segments of its trajectory,

the MDH may not be in contact with any DG Therefore, only a fraction of the move time

Tm can actually be used for data collection. Let T p be the productive move time, which
is the time during which the MDH is in contact with at least one DG. T p can be

computed by multiplying the number of Navigation Agents N w with the duration for

which data can be collected when the MDH is in contact with at least one DG in the

101

vicinity of each Navigation Agent. The contact distance with at least one DG in the
vicinity of a Navigation Agent can range from 2r (when a Navigation Agent and all its
DGs are collocated) to 4r (when at least two DGs of a Navigation Agent are located on

two diametrically opposite points on the circle of radius r around the Navigation Agent)

leading to an average of 3r. Therefore, T p can be expressed as N w x 3r / v. Given that the

number of Navigation Agents NW can be computed as L/(2k+l)r , Tp can be

3L

T =—— _
p (2k +1” ...... (Eq. 6 3)

rewritten as:

Let Tu be the amount of time required to upload all the data accumulated at DGs

around a Navigation Agent. If duration T p is insufficient to collect data accumulated

around a Navigation Agent, then the sink stops to collect the remaining data. So, tea” is

the sum of the time required to physically complete the trajectory and the stop times.

Therefore, too” (see Figure 6-2) can be written as:

Tm + (Tu — T otherwise .......... (Eq. 6-4)

_ {Tm if(Tu .<_ Tp)
tcoll - p

In Eq. 6-4, Tm represents the portion of the trip collection-time for which the sink is

moving and (Tu —Tp) represents the stop duration. When the amount of data to be
collected from the DGs is small (Tu S T p), the stop component is zero. In other words

the collection time is same as the sink moving time Tm . This scenario represents the

Collect-on-Move operating mode. When (Tu > T p) the stop duration is non-zero,

representing the Stop-on-Demand collection mode.

102

representing the Stop-on-Demand collection mode.

As pointed out in Section 6.4, the value of the hop-bound factor k impacts the amount
of data that is collected from the Designated Gateways once every collection cycle (t).
As the value of k increases, the amount of data to be collected also increases until stops

are required to complete the data collection from DGs around a Navigation Agent. The

lowest value of k for which stops are required is referred to as the stop threshold k mp .

6.6.2.1 Numerical Evaluation of Trip Collection-time

Using Eq. 6-4, we obtained the trip collection-time for a circular network terrain of
radius 300m and sensor transmission range of 30m. The minimum number of hops

(i.e. kcritical ) at which static sink Operation is possible is 300/30 = 10. The collection

cycle 1' was set to 9005ec and the MDH speed v was set to 3m/s.

Figure 6-6 and Figure 6-7 show the quantities Tm , T p , Tu and the resultant trip
collection-time ta,” with varying k, and for different values of 2.. With increasing k,
initially the quantity Tm decreases quite sharply, and then the rate of decrease reduces
until k reaches akcn-tical , at which point the sink becomes static (Figure 6-1:a). This is
because with increasing k, the sink trajectory includes fewer and fewer Navigation Agents
and hence it becomes shorter. The quantity T p goes down at a much faster rate because
as the Navigation Agents are spread farther apart, the MDH is not in contact with DG

during an increasing duration while in its trajectory. Tu , on the other hand, depends on X

but does not vary with k.

103

    
 

 

 

 
  

 

 

   
  

 

 

 

3000 -
2500 -
8 2000 -
L”,
+3 Tp
a 1500 -
".
'—
1000 - Tu()\=56 bps)
e—a - imi—h—A
500 -
Tu()\=8 bps)
tum-"HA"
0 . .
0 1 2 3 4 5 6 7 8 9 10
Hop-bound(k)
Figure 6-6:Impact onkonTm, T p and Tu
3000 -
2500 - _ Wm ._.
Zn; :.:..:?§s‘t0petlirésh'old{3:52
.3 2000 - 'I “—56 st) :9,» 5.1,,t0r5-lhr9550'di:
g (5:3 bps) _:-_
_o
is
:5 1000 -
0
_D.
.E 500 -
(A=8 bps)
O I I I I I I l I I I

 

 

0 1 2 3 4 5 6 7 8 9 10
Hop-bound (k)

Figure 6-7: Impact of k on trip collection-time for different data rates

For all k, as k increases, the collection-time goes down fast until the stop threshold

104

kstop is reached, and then with a slower rate until ken-flea, is reached. tea” is initially

dominated by Tm, but as T p drops below Tu , the condition too” > Tm as in Eq. 6-4

holds good. At k = ken-Hm, , tea” is same as Tu . For xi. = 8 bps, the values of k < 4

result in trip collection-time greater than t. For k < 6 , the MDH can collect data
without stopping because T p > Tu and therefore k stop = 6 . For k > 6, the MDH has
to stop and collect data. In contrast, for higher data generation rate ,1 = 56 bps, the stop
threshold kstop = 2. Thus, in the higher data generation rate scenario, the stop threshold

occurs for a relatively smaller value of k.

6.6.3 Energy Consumption

 

 
 
 
  
  
   
 
 

Designated Gateways

F" sub-trajectory
( radius = R )

Navigation
Agents

1
Nodes send data I"
to DG from up to /3"*
k-1 hops

I
I

Annular

—"

a
o._
u. ,
------
-----------

 

 

 

Figure 6—8: Data Aggregation at DGs (l-hop nodes of Navigation Agent)

Network life is closely related to two energy metrics, namely, the energy spent per

PaCket and the standard deviation of energy drainage across all nodes. Note that the

105

energy metrics are not dependent on the specific data collection mode, because energy
consumption is only affected by the depth of the DG trees and the number of packets
generated in the network. It does not depend on whether the sink had to stop or not for
uploading data fi'om the DGs.

In our analysis, the DGs are chosen to be all the l-hop neighbors of Navigation Agents.
An alternative approach would be to assign all l-hop reachable nodes from the MDH to
be the DGs along the full trajectory. An advantage of this approach is that it results in

higher productive time T P with T p = Tm in the best case. We have used the first

approach to DG selection for easier analytical tractability of the energy model developed
in this section. Regardless, the analysis performed in this section holds for any DG
assignment for the reasons outlined in Chapter 9 and Chapter 10.

Figure 6-8 shows a zoomed-in segment of the ith sub-trajectory with radius R ,-.

Depending on its origin, a data packet has to travel between 1 to k hops before it is
collected by the MDH from a DG Energy required to collect a packet that was routed

l-hops can be written as Etx +(l—1)(E,x +E,.x ), where Ea and Erx indicate the

energy expenditures for transmitting and receiving a packet respectively. Around each
Navigation Agent, there are annular regions corresponding to the number of hops to reach
the MDH. An example of such annular region for k = 2 is shown in Figure 6-8. The

number of packets generated in an annular region defined by radii (1 —1,1) is a function

of the number of nodes in that region and their data generation rate, and can be written as
”[002 —(r(l —1))2 ]d x xi. , where d is the node density. Considering l-hop routing for

those packets, the total energy expenditure is:

106

7r[(rl)2 —(r(l —1))2] x d x ,i x {Etx + (1 —1)(E,, + Eng} ...... (Eq. 6-5)

Average Energy Per Packet (AEPP) within a Navigation Agent’s jurisdiction (the area
from which packets are collected at the DGs of that Navigation Agent) can be computed
by dividing the total energy expenditure by the total number of generated packets. It can

be written as:

E H002 _(r(z -1))2] x d x k x [15,, + (1—1)(E,x + 5,, )1}

 

 

 

 

 

 

 

AEPP= 1:1 2
7r(kr) xdx/i,
which can be simplified as:
k 2 2
)3 Hot) _ (,0 -1» ][1E,, +1E,, -Erx]}
[ =
AEPP = ......... (Eq. 6-6)
”(1:02
9 ' 180 l
E 8 - a 160 -
0. m
E; 7 1 f, 140 -
:1 or
£2 6 - ,2 120 -
3 S
2.; 5 ' E 100 ‘
0- 4 - g 80 -
E u.l
g 3 1 “5 60 -
I“ >'
g, 2 - g 40 -
E .
. 'o .
3 1 (75 20
0 ' I I I l r O '
0 2 4 6 8 10 0 2 4 6 8 10
(a) Hop-bound (k) (b) HOP-WWIId (’0

Figure 6-9: Impact of k on energy metrics

107

Assuming spatially uniform node distribution, which results in unifomly distributed
Navigation Agents, the expression for AEPP in Eq. 6-6 also holds for the entire network.
As shown in Figure 6-9:a, the energy required to deliver packets to MDH increases
non-linearly with k. Eq. 6—6 also indicates that the data generation rate I. and node density
d do not impact AEPP since the cost incurred by a packet is only decided by k, which
indicates how far it has to be routed in the worst case.

Assuming that the tree under each DG is balanced, the number of nodes at each level
of the tree is evenly distributed across all DGs. The energy drainage at each node is due
to a) transmissions of its own originated packets, and b) routing packets from nodes that
are farther away from the DOS. The variability in energy expenditure is mainly caused by
the routing component. Sensors that are k-I hops away from a DO, i.e. k hops from the

corresponding Navigation Agent, incur just the transmission cost for their own packets.
Sensors in the annular region A defined by radii (l—l , I ) have to bear the routing cost of

packets fi'om nodes that are in the annular region defined by the radii (l ,k ). Thus, the

routing burden on the nodes in the annular region A can be written as:

Emu-"g = 7r((kr)2 — (lr)2)x d x ,i x (5,, + (1—1)(E,, + Em»

Assuming that the routing is load-balanced and all nodes in region A experience equal

routing burden, the routing energy drainage in region A can be written as:

7r((kr)2 —(1r)2]x d x xx (Eu + (i—1)(Etx + 15,,»

fll(lr)2 - ((1 — l)r)2 Jx d

 

El,r0uting =

which can be simplified as:

108

2 2
1k —1 E + l—l E +E
El,routing= ( )( 721:1) )( ’x ’7‘» ....(Eq.6-7)

 

Eq. 6-7 implies that routing energy drain for any node depends only on its position in

the tree rooted at a DG, the value of kwhich defines the depth of the tree, and the data
generation rate it. It does not depend on node density because with increasing density, the
number of DGs increases proportionately, resulting in more DG trees, but the depth of the
tree does not change. With increasing it, the average routing load experienced by nodes

increases. The mean routing energy drainage for a node is given

 

k
,. l
by: Emitting";X ZELrouting. For a DG, the Standard Deviation of Energy Drainage
[:1
(SDED) can be computed as:
k 2
SDED = 1/ k x z [El,routing _ Erouting]

1 =1
which is plotted in Figure 6-9:b. Observe that the quantity SDED increases non-linearly

with k. This increase pattern can be explained by the non-linear increase in multi-hop
routing (i.e. a DG’s tree size) with increasing k. The quantity tea”, however, stops
decreasing substantially once the multi-hop routing indicator k reaches the

threshold kstop. This is apparent from Figure 6-7 in Section 6.6.2.1. These suggest that

the high energy cost for large k is not quite offset by the reduction in the trip collection

time. Therefore, values of k below kstop should serve as a reasonable range from which

k can be chosen for striking a reasonable balance between the collection delay and

network life.

109

6.6.3.1 Impacts of Data Collection Modes on Energy

The above energy analysis has been performed for stable data collection when the
MDH is operating within the feasible region of k as defined by the stop and the stability

thresholds. Hence, the Collect-on-Move MDH is assumed to operate between k gammy
and kstop and a Stop-on-Demand MDH operates beyond k mm”), . Under such stable

operations, all the data accumulated at the DGs during a collection cycle is collected by
the sink during that cycle. As a result, the energy metrics developed in the previous

sections hold regardless of the specific data collection modes.

6.7 Applicability of Model to Non-circular Covering Trajectories

Area coverage and minimization of the MDH travel distance are two primary factors that
drive the choice of trajectory pattern. Concentric circular and spiral trajectories have been
found to be best for achieving high coverage while minimizing the travel distance.
However, there are a number of other practical considerations that can influence the choice
such as minimizing the number of turns, energy required for special maneuvers and the
like. Such considerations may dictate the choice of a different covering trajectory for
specific mobile sink deployments. Thus, if a different covering trajectory is chosen, the
analysis performed in this section still applies with minor modifications.

Figure 6-10 shows some example trajectories that can be used to achieve area coverage
and 55 represents the separation distance between sub-traj ectories. The top graph (Figure
6-10:a) shows the concentric circular sink trajectory which consists of multiple circular
sub—trajectories of different radii. In the middle graph (Figure 6-10:b), the sink trajectory
traverses the network fi'om side to side. In the bottom graph (Figure 6-102c), the sink

traverses the terrain following a spiral trajectory. If a different sink trajectory is used, Eq.

“0

6—1 will need to be modified appropriately and the resulting trajectory length, L (reported in

Appendix B) should be used for the rest of the derivations in Section 6.6.2.

 

 

(a) 9000 -

aooo -
E 7000 -
z =500m, r =50m
a 6000 -
U)
E, 5000 «
_J

. e .000.

O
‘6 3000 ~
.2
g 2000 -
’— .

1°00 R=400m, r=50m

o 1 2 3 4 5 s 7 0 9

Hop-bound (k)

  
  

 

 

 

 

 

 

 

 
   
 

 

 

 

 

 

 

 

 
 
   
  

 

 

 

 

9000 -
E 8000-
7000 -
%, R=500m, r=50m
g 6000 «
_I
P; 5000‘
.9
a 4000 <
S 3000 ~
2000-
1000 -
R =400m. r =50m
0 1 2 3 4 5 6 7 a 9
Hop-bound (k)
9000 «
A 8000 -
E
V 7000 -
c R =500m, r =50m
*5, 6000 - /
S
_, 5000 -
Br 4000 -
.9
_g 3000-
E 2000 -
i.—
1000 ~
0 R=400m, r=50m
0 1 2 3 4 5 e 7 8 9
Hop-bound (k)

 

 

 

 

Figure 6-10: Impact of k on trajectory length, L

Figure 6-10 shows the length of the sink trajectories for different values of k for the

111

chosen heuristic methods for different ratio of topology radius R and transmission range r

and 63 = 2kr . In all cases, results show that as the extent of multi-hop routing increases,

the trajectory length shrinks. This trend was found to be valid for trajectories other than

those shown in Figure 6-10 and other values of 6 S .

6.8 Summary and Conclusions

In this chapter, a generalized formulation of the data collection problem has been
developed that encompasses both static and mobile sink scenarios. The formulation uses
a tunable hop-bound factor (k) which represents the amount of allowed multi-hop routing.
The ranges of k appropriate for real-time and non-real-time data collection using static
and mobile sinks were analyzed. A model was developed based on the proposed

generalized fi'amework and expressions were derived for trip collection-time and energy

metrics. In addition, the threshold of k referred to as k stop which would require the

mobile sink to switch from Collect-on-Move to Stop-on-Demand mode was identified.
The generalized formulation for data collection proposed in this chapter can be used to
assess relative merits of static and mobile sink data collection options for given
application, network and MDH parameters. When mobile sink option is considered, the
degree of multi-hop routing that can be sustained without incorporating stops in the MDH
trajectory can be determined. This is an important consideration for sinks such as
fixed-wing UAVs that cannot hover or stop as mentioned earlier. Numerical evaluation of
the trip collection-time and energy metrics shows that as the hop-bound factor increases,
the trip collection-time decreases at the expense of energy performance. This confirms
that the hop-bound factor k can be used as a knob to achieve the desired energy-delay

balance in the context of mobile sink data collection.

112

Chapter 7. Network-Assisted Data Collection (NADC)

In Chapter 6, we presented a generalized formulation for the data collection problem
using a hop-bound factor k which can used to achieve the desired energy-delay balance.
In this chapter, we develop a distributed network-assisted solution architecture for
non-real-time data gathering to realize the generalized k-hop based data collection
framework shown in Figure 6-1 :d.

7.1 Concept Summary

Instead of sensors’ geographical location information, the proposed navigation

mechanism uses network connectivity information for MDH navigation. The key

components of the navigation and data collection mechanism are as follows.

 

 

 

 

 

 

 

 

 

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N O K O N ~@K
Step 1 : Identify Navigation Agents Step 2 : Identify intermediate Navigators
. Navigation Agent @ lntennediate Navigator

 

Figure 7-1: Logical overlay topology construction for k=2

Phase 1: A logical overlay graph is created (see Figure 7-1) which is composed of two
kinds of nodes — Navigation Agents (NA) and lnterrnediate Navigators (IN). The MDH is

navigated through the NA nodes, and the IN nodes act as the intermediate MDH routing

113

entities between two NA nodes. The l-hop neighbors of the NAs act as the Designated
Gateways by accumulating and buffering data from the other nodes and uploading it to
the MDH. The construction of the overlay graph is accomplished in following two steps.

S_teLI: Navigation Agents are selected such that by passing through them, the MDH is
guaranteed to traverse a trajectory that ensures that all nodes can be reached within a
maximum of k hops. Navigation Agents ensure that data is collected from all the nodes in
their vicinity and guide the navigation of the MDH. Thus, a spanning trajectory is
accomplished by the MDH without having to pass through the close proximity of each

individual sensor node. This is the key to reducing the trip collection-time ta,” in the

proposed navigation mechanism. Figure 7-1 shows a network of sensors where Nodes A,
E, I and N are identified by the network as the NA nodes. The value of k is set to 2 in this
example. Note that by visiting the identified NA nodes, it is possible to collect data from
all nodes in the network using routes with 2 or less hops.

S_teL2: A set of Intermediate Navigator (IN) nodes are selected for guiding the MDH
between the Navigation Agents. This is needed since the NA nodes may not always be
radio neighbors to each other. The IN nodes act as the intermediate MDH routing entities
between two NA nodes. Nodes that are on the shortest path between two NA nodes are
identified as the IN nodes. In the network shown in Figure 7-1, nodes B, C, D, F, G, H, J,
K, and M are identified as the IN nodes.

Phase 2: A distributed Traveling Salesman Problem (TSP) mechanism is used to compute
a path that traverses through all the NA nodes. The TSP solution is computed on a
graph consisting of NAs as the vertices, and link costs are defined as the hop counts of

the shortest paths between the NA node pairs. Two NA nodes are considered connected

114

only if they are at most 2k+l hops apart. Figure 7-2 shows a TSP path that is

computed for the example network.

 

. Navigation Agent

@ Intermediate
Navigator

DG (encircled
nodes)

 

 

TSP Path
(MDH Trajectory )

 

 

 

 

Figure 7-2: Network computed TSP path covering NA nodes

Phase 3: A runtime navigation system is designed for physically routing the MDH along
an appropriate sequence of the NA and 1N nodes along the computed TSP path so that the
network wide collection-time for the MDH is minimized. This component of the MDH
navigation process is similar to network layer routing except that in this case the MDH is
physically routed through the NAs and the [NS as opposed to the packet routing through
multiple routers. The NAs and INs on the network-computed path guide the mobile data
harvester on a trajectory for data collection through the network as indicated in Figure
7-2.

Phase 4: The first-hop neighbors of the chosen NAs declare themselves as Designated
Gateways. When k=1, all data is uploaded to the MDH using single hop transmissions.

But, when k >1, nodes that do not come in direct contact with 3 Navigation Agent need to

115

route their data to an appropriately chosen Designated Gateway using multi-hop paths so
that the data may be buffered and uploaded to the MDH. Therefore, routing paths need to

be chosen from each sensor node to an appropriately selected Designated Gateway.

7.2 NADC: Algorithmic Details
7.2.1 Construction of Overlay Navigation Topology
The choice of Navigation Agents and the Intermediate Navigators on the overlay

logical topology will have significant impact on the MDH’s round trip tour time tea” , and

the subsequent delivery delay. The problem of finding the smallest set of NAs by visiting
which the MDH would be guaranteed to have passed through k-hop topological distance
of all network nodes can be formulated as the minimum k-hop dominating set finding
problem [145] in a connected graph. Since finding the minimum dominating set is known
to be NP-Hard, we have adopted a heuristics based approach based on [146] as follows.
Tree Creation: A network-wide tree is created using an arbitrarily chosen root node. At
the end of the tree creation process, every node knows its distance to the root and also
those of its neighbors.

Navigation Agent Identification: This algorithm identifies the navigation agents based
on the logical overlay topology construction as outlined in Section 7.2.1. After a node
determines its distances from the root, it declares itself as a ‘Navigation Agent’ by
sending a ‘Declare-NA’ message with TTL (Time to Live) value set to k—l. All nodes that
receive this declaration message accept the Navigation Agent as their parent NA,
decrement the value of the TTL and re—broadcast the ‘Declare-NA ’ message. If a node
finds the received TTL value to be zero, the receiving node sends out an ‘Accept—NA’

message, otherwise, they decrement T‘T‘L and re—broadcast the message. A node that

116

receives an ‘Accept-NA’ message with TTL=O from all of its neighbors that are closer to
the root (have lower hop-count to the root), declares itself as a ‘Navigation Agent’. This
process continues until all nodes are marked either as a Navigation Agent (NA) or as
Covered. The actions taken by nodes when they receive the ‘Declare—NA’ and

‘Accept—NA’ messages are summarized in the pseudo code in Figure 7-3.

 

Algorithm for Navigation Agent Identification:

Initializations for NA identification process:
Set number of Accept-NA messages received to zero

Process Declare-NA message from a neighbor:

Mark neighbor as a Navigation Agent in neighbor table

if ( ttl value in Declare-NA message = O)

9 Broadcast an Accept-NA message to all neighbors with TTL = k-l
10 else {

ll Decrement TTL value in Declare-NA message
12 Re-broadcast Declare-NA message

13 }

14

15 Process Accept-NA message from a neighbor:
l6 Iower_neighbor_count = number of neighbors with lower hop count to root node
17 if ( TTL value in Accept-NA message = O) {

l 8

19 if ( number of Accept-NA messages received = Iower_neighbor_count) {

20 //none of my neighbors is an NA, so declare myself as one
21 Mark myself as a Navigation Agent

22 Broadcast a Declare-NA message to all neighbors with TTL = k-l

23 } else {

24 // keep track of how many Accept—NA messages were received
25 Increment Accept-NA messages received

26 }

27 // ttl value is greater than 0

28 } else {

29 Decrement TTL value in Accept-NA message

30 Re-broadcast Declare-NA message

31 }

 

 

 

Figure 7-3: Pseudo code for Navigation Agent identification

117

These self-elected NA nodes represent a minimal dominating set so that the k hop
neighbors of all the NA nodes include the entire node population in the network. This
implies that if the MDH trajectory visits all the NA nodes, then it is guaranteed to be
passing through the network such that it is within k-hop distance of all nodes at least once
during its trajectory. Therefore, in order to collect all network data using up to k-hop
routing, it is sufficient for the MDH to traverse through the virtual topology formed by
the NA nodes that are self elected using the distributed process as outlined above.

By definition, the Navigator Agent nodes are at least k +1 and up to 2k+1 physical

hops away from each other. As a result, while performing the MDH navigation, the data

harvester needs to be physically routed through k +1 to 2k +1 Intermediate Navigator
(IN) nodes. For example nodes B, C and D in Figure 7-1 act as the IN nodes while
physically routing the MDH between the NA nodes A and E. Similarly, nodes F and G act
as the IN nodes while routing the MDH between the NA nodes E and I. To enable the
MDH navigation, a set of such IN nodes need to be selected for completing a round trip

trajectory across the network.

Intermediate Navigator Identification: The IN nodes (k +1 to 2k+l) between two
NAs can be identified by finding the nodes on the shortest hop physical routes between
the NA nodes. To identify such INS, each NA constructs a minimum hop tree rooted at
itself and spanning up to a depth of 2k+1 physical hops. A standard beacon based tree
creation process is used for this step. Once created, such trees represent the minimum hop
routes between every NA neighbor pairs on the logical topology. Nodes on the route
between such NA neighbor pairs are then identified as the Intermediate Navigators. The

NA and IN node identification completes the distributed construction of the overlay

118

navigation topology, as demonstrated in Figure 7-1.
7.2.2 Distributed Trajectory Computation

The goal of this algorithmic module is to compute an MDH trajectory that minimizes
the traveled distance while passing through all the NA nodes. The trajectory computation
can be formulated as a Traveling Salesman Problem (TSP) on the overlay topology
consisting of the NA nodes. The objective is to obtain a minimum cost tour of the NA
nodes such that each NA is visited exactly once before returning to the starting point.
Cost of the links in the overlay topology represents the shortest distance between the NAs.
Distance is measured in terms of hop count as an estimate of the physical distance.

An Ant Colony Optimization-T SP solution as proposed in [147] has been adapted for
this step in the following manner. The underlying idea is to use exploratory ant packets to
search for different round trip solutions to the TSP and then to use a positive feedback
mechanism for reinforcing good quality solutions represented by the trajectories of
exploratory ant packets that successfully complete the full round-trip with desirable trip
costs. Ant packets are originated at each NA and they build solutions to the tour by
moving from an NA to another until all NAs are visited. Note that ants are routed
between NAs using multi-hop paths consisting of the Intermediate Navigators. Ants that
prematurely return at its starting NA without visiting all NA nodes are discarded. After
the completion of A a complete tour during the search round, an ant makes another
reinforcement round along its already traversed path and lays a certain quantity of
pheromone on each edge of the path depending on the total cost of the tour. This
process serves as the positive reinforcement of a path found during the search round

based on its quality. During the search phase, when an ant anives at an NA node, it

119

selects the next NA node to visit based on the pheromone that has already been deposited
on the outgoing edges fi'om the current node. To summarize, at any point in time the
pheromone trail on an edge represents the knowledge that has been accumulated about
the desirability of the edge in the TSP solution.
7.2.3 Run-time Navigation (RN)

For navigation purposes, the fi'amework relies on an accurate Direction of Arrival (DoA)

[148] detection capability using phased array antenna systems at the MDH.
Send Nav-Query to a NA Collect data from registered
node in vicini . nei hbors while movin

Recv. Na
Reply from
first NA

 

 

 

   
        
     
  
 

contact-next-
node-timer
expiry

     
   
  
 
 

 
  

Recv Nay-Reply
From IN

Recv. Nav-Reply
from NA

  

 

1. Send Transmit- . Begin moving
Data message to towards IN node.
nodes indicated in . Start next-node-
Nav—Reply. query-retry- contact-timer.

. . timer expiry

. Begrn movmg
towards NA node. 1
. Start next-node-

 

 

 

 

. Send Nail-Query to next
contact node
2. Start query-retry-timer

contact—timer.

 

 

 

 

 

 

Figure 7-4: State machine for navigation and data collection

The MDH starts its navigation from an arbitrary location in the network. Initially, it
collects neighborhood information from periodic hello messages broadcast by the sensor

nodes. Once it receives a hello from an NA in the vicinity, the MDH sends a ‘Nav—Query’

120

message, as a response to which the NA sends a ‘Nav—Reply’ back to the MDH. Upon
receiving the Nev-Reply message, the MDH enters the NAVIGATION-BY—NA state as

shown in Figure 7-4.

 

   
 

Send and Receive Nev-Reply

from C
Send Nev-Query to C \ x
Send Nav-Queryto G x @ ----- @ iiiiiiiiiii Q
Rece$ve Nev-Reply

Send and
Receive

from
Receive Nev-Re \ I
from X ply\‘ OE Q’g‘g’éeply

A
V

/'1

 

 

 

Receive Nev-Reply
from F O
. NavigationAgent @ Intermediate Navigator

 

Figure 7-5: Navigation of MDH from NA node F to C (k=2)

The Nav—Reply message contains the following information:

1. Trajectory related: The next NA on the trajectory that the MDH
should visit, and the next IN node that will be able to route the MDH to that
next NA node.

2. Data collection related: A list of all the NA’s l-hop neighbors from
which the MDH needs to collect data when it is present in the vicinity of this

particular NA.

121

3. Direction related: While receiving the Nav-Reply message, the
antenna system of the mobile data harvester evaluates the signal ’5 Direction
of Arrival (DOA) [148] so that the MDH knows which direction to move to
get closer to the NA. In the absence of any location information, this DOA
based mechanism provides an approximate yet practical way for navigation
in the present framework. I

The MDH determines the DOA of the Nav-Reply message, and based on the direction,
starts moving towards the NA. It starts a contact-next—node-timer which expires after the
MDH has traveled for a certain distance. The MDH then starts data collection fiom the
nodes registered with the NA by sending a Transmit-Data message. The Transmit-Data
message is broadcast periodically and enables data transmission from individual nodes in
the neighbor-list in the Nav-Reply message. This mechanism ensures that only the nodes
in the list transmit data to the MDH until the MDH makes contact with the next NA.
After completing these actions, the MDH transitions to the COLLECT-DATA state.
Figure 7-5 illustrates the sequence of events during the navigation of the MDH between
two NA nodes F and C using three IN nodes X, Y and G. The sequence starts with the
reception of the Nav—Reply from node F. The MDH enters NAVIGATION-BY—NA state
and performs related actions as described above.

In the COLLECT-DATA state, the MDH collects sensed data from the nodes. Once
the contact-next-node-timer expires (point 2 in Figure 7-5), the MDH moves to
CONTACT-NEXT_NODE state and sends a Nov-Query to establish contact with the IN
node indicated in the Nav-Reply message from the NA. It repeats the Nov-Query message

until it receives a reply from the next contact node. In the example, the MDH sends the

122

Nov-Query to IN node X. Once it receives a reply message from the IN node, the MDH

moves into the NAVIGATION-BY-IN state.

 

Network-assisted Runtime Navigation and Data Collection:

1

2 Process Nov-reply from current_host_node:

3 //initializations based on Nev—reply

4 next_host_node = Nav-reply_msg(next_node_to_visit)

5

6 If (current_host_node is Navigation-Agent) {

7 fl Nav-reply contains list of neighbors

8 Neighbor_List = Nav-reply_msg (neighbors (current_host_node))
9 flCollect data from all of its neighbors
10 For (each neighbor in Neighbor_List) {

11 Set Tx-Enabled bit in T ransmit-Data message

12 }

13 Set Tx-Enabled bit for current_host_node

14 Send T ransmit-Data message

15 Move towards new_host_node(Nav-reply_msg)

16 Send direction_query to new_host_node

17 } else { // current_host_node is Intermediate Navigator
18 S1; Move towards current_host_node(Nav-reply_msg)

19 Send Nav-Query to new_host_node

20 If (N av-reply_msg not received)

21 go to SI

22 }

23

24 Move towards a host_node Wav—reply_msg):

25 Find angle of arrival (DOA) of Nav-reply_msg

26 Set destination to x,y in the direction of DOA at distance = delta
27 Physically move to x,y

28

 

 

 

Figure 7-6: Collect-on-Move algorithm description

Actions in the NAVIGATION-BY-IN state are similar to NAVIGATION-BY-NA.
Again, the MDH determines the DOA and starts moving towards the IN node. It starts a

contact-next-node-timer and enters the COLLECT-DAT A state and continues to collect

123

data. Upon timer expiry, it contacts the next-contact-node indicated in the Nov-Reply
from the IN node. The reply message from the IN node could have contained another IN
node or a NA node as the next contact node on the TSP path. This is because any two NA
nodes can be either k+1 or 2k+l hops away from each other. In the example in Figure
7-5, the Nav-Reply fiom X contains IN node Y as the next contact node. So, in this case,
the MDH enters the NAVIGATION-BY-IN once more before going to
NAVIGATION-BY-NA. If the next contact node is an NA node, then it enters the
NAVIGATION-BY-NA and repeats the actions described earlier. Thus, data collection
from the nodes registered with NA takes place from the time the MDH receives a
Nov-Reply from the NA to the time it receives a Nov-Reply from the next NA. In Figure
7-5, the registered neighbors of F are allowed to transmit data to the MDH between

points 1 and 10.

7.2.4 Designated Gateway (DG) Identification and Routing

One hop neighbors of Navigation Agents act as the DGs. After an NA is identified
according to the algorithm described in Section 7.2.1, all its one-hop neighbors declare
themselves as DGs. When the MDH visits the Navigation Agents, it is able to directly
collect data from the Designated Gateways using a single hop transmission. The NA

identification process also ensures that every sensor node has at least one DG within

k — 1 hops and at least one NA within k hops.

Sensor nodes that do not directly come in contact with the MDH route their data to the
closest DG. The route from any node to the NA is found using the NA-rooted trees during
the Intermediate Navigation identification process mentioned in Section 7.2.1. Along

the route to an NA, 3 packet from a sensor node gets to a DG along the path where it is

124

buffered. The idea behind buffering data at the Designated Gateways rather than at the
Navigation Agents is two-fold. First, it saves energy by eliminating one extra
transmission for every data packet. Second, it helps to prevent buffer overflow that can
occur if all DGs route packets to their NA for buffering until the MDH comes into range.
The choice of the Designated Gateway to which a node routes its data can be based on
various criteria. For instance, the hop-count to DG, the residual energy of the DGs, the
quality of the link between the DG and the MDH, etc. In this work, we have chosen to

have nodes send their data to the closest Designated Gateway node.

7.3 Summary

In this chapter, we developed the distributed protocol and algorithms required to
perform network assisted data collection called Network-Assisted Data Collection
(NADC) based on the generalized fiamework proposed in Chapter 7. NADC does not
rely on the availability of localization services for the sensors and the MDH. The

various components of NADC and the algorithmic details were presented.

125

Chapter 8. Network Assisted Data Collection: Performance
Evaluation

In Chapter 7, a distributed network assisted data collection framework for mobile data
harvesters was developed and the required algorithms were described in detail. The
performance of the proposed NADC framework has been evaluated through an NS2
simulation implementation. The details of the experiments and the results obtained are

presented in this chapter.

8.1 Simulation Setup

The simulation model represents sensor networks deployed for monitoring
applications that generate data at constant rate. The sink represents mobile devices such
as Unmanned Ground Vehicle (UGV) or mobile robot. Both sensors and the MDH in our
experiments are assigned a communication range of 30m. Each node generates data at a
constant rate of one 128 byte packet per second, and makes transmissions using an IEEE
802.11[l49] MAC protocol. The speed of the mobile data harvester is set to 3m/s which
is the typical speed of a mobile sensor system [83]. Each experiment was run for
duration of 1200s to compute the collection statistics.

8.1.1 Implemented Protocols

The following mechanisms have been implemented.
Network Assisted Data Collection (NADC): The following flavors of the proposed
NADC framework based on the mechanisms presented in Chapter 7 have been
implemented.
1. Centralized (CENT): Centralized versions of the navigation overlay construction and
the TSP-based MDH trajectory computation as presented in Sections 7.2.1 and 7.2.2. This
scheme is expected to provide a performance upper bound for the hilly distributed

126

NADC.
2. Distributed with Minimum Dominating Set Overlay (D-MDO): Corresponds to the
distributed solution for minimum dominating set based navigation overlay computation,
ANT TSP based MDH trajectory determination, and runtime navigation as presented in
Sections 7.2.1, 7.2.2 and 7.2.3. This is the completely distributed version of NADC.
3. Distributed on Physical Network Topology (D-PNT): In this na'r've approach, no
navigation overlay is computed. As a result, all network nodes act as navigation agents.
Therefore the MDH has to pass through each individual node as opposed to going only
through the overlay nodes used in the other strategies. The performance of this
mechanism is expected to provide the lower bound for NADC.
Random Walk (Efl): A Random-Walk-based mobility strategy from [106] has been
implemented. The key idea is that after every pause, the MDH randomly picks the
direction and travels for a pre-set distance equal to the communication range. While
traveling along the‘chosen direction, the MDH collects data from the nodes that come in
contact using k-hop paths.
Area-Scan: Given the dimensions of the sensor field, a centralized entity computes a
trajectory to scan the entire area. The trajectory depends on the dimensions of the sensor
field, the sensor communication range and the value of k. This scheme does not require
the sensors to have localization information.

In the case of Area-Scan and RW, as the MDH moves, it sends out k-hop beacons in
response to which all nodes within k—hop distance forward the data stored in their buffers
to the sink. So, unlike NADC, data is not buffered at the DGs prior to the arrival of the

MDH.

127

8.2 Single Hop Data Collection (k=1)

In a 150m x 150m sensor field, 60 nodes were placed such that they form a connected
network in various configurations namely three weak clusters forming a triangle (3-Trz),
three clusters placed in a line along the diagonal (3-Line), nodes distributed around voids
(Void), uniformly distributed (Uni) and placed in a lattice (Latt) as shown in Figure 8-1.

Each node has been configured with a data buffer of 200 packets.

 

 

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0

0
O 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0

O I o
0 ° o ° ° °

Void Uni Latt

 

 

 

 

 

 

 

 

 

 

Figure 8-1: 60 node topologies used for experiments

The average delivery delay for the evaluated protocols is shown in Figure 8-2. We use
D-PNT as the baseline mechanism to compare with the delivery delay of the other
protocols. The results indicate that choosing a subset of nodes to visit for the purpose of
data collection brings down the delivery delay significantly for all of the different
topologies considered. For instance, for 3-Tri topology, the CENT solution reduces the
delay by 59% compared to the D-PNT topology tour. In CENT, the identification of the

Navigation Agents and TSP solution on the corresponding overlay are computed centrally

128

with global topology information. As a result, it performs the best thus providing an
upper bound. For the same 3-Tri topology, the D-MDO solution, which is a fully
distributed counterpart of CENT, achieves 54% delay improvement. Observe that the
delay performance achieved by D-MDO is better than D-PNT and very comparable to
CENT for all the clustered topologies. It was found that the delivery delay is directly
related to the trip collection-time and hence the round trip distance of the MDH

trajectory.

 

140 -_. CID-PNT BCENT ID-MDO BRW SIArea-Scan

 

        
    

   
 
   
    
   
  
    

     
     

 

 

 

 

 

    

      

 

 

120 "-9"??? ........................................................
Random-Walk Area -Scan
1; 100 ------------------------------
a
a 80 --------------
'6 / 7V

 

Uni Latt Void

 

 

 

 

Figure 8-2: Delay performance for various topologies

Figure 8-3 shows the trip collection-time and round trip distance for all protocols
except RW, since these metrics do not apply to random-walk-based mechanisms which
are not guaranteed to complete round trips. Area-Scan scheme shows a steady delivery
delay because the trajectory followed by the sink is the same for all the topologies since
the dimension of the sensor fields is the same in all cases. It can be observed that the

129

trends for the delivery delay (except Area-Scan) follow the collection-time and distance
very closely. This is because data is collected from a node periodically when it visits the

node’s parent NA approximately every trip collection-time or icon. Upon firrther

investigation, we found that the collection-time is actually related to the number of
Navigation Agents identified during the overlay construction phase. For example, the
mnnber of NAs identified for 3-Tri topology by CENT and D-MDO were 7 and 10
respectively. In the case of 60 node Uni topology, CENT and D—MDO identified 9 and 16
NAs respectively. Higher number of NAs gives rise to longer trips and subsequently

higher packet delivery delay.

 

 

 

 

 

 

 

 

      

 

 
   

    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

330 -— UD-PNT 1030 — UD-PNF scram
ID—MOT BArea-Scan
nCENT — 930 _
280 ID-MDO _____
ISIArea-Scan 830 a
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B 130 2'? E E l— g
o a a :: =
30 3 :‘ ' :- 30 ,_ :E .-':
3-Tri 3-Line Latt Void 3-Tri 3-Line Uni

 

 

 

 

Figure 8-3: Trajectory indices — collection-time and distance

130

MDH Trajectory - D-PNT

 

 

 

 

 

 

 

 

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O 51 O
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0 56 C: r 502 r if? 34
0 41 5O 1 00 1 50

Figure 8-4: MDH trajectories for 60 node uniform t0pology

131

Figure 8-4: MDH trajectories for 60 node uniform topology (contd.)

 

 

 

 

 

 

  

 

 

 

MDH Trajectory - D-MDO
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132

The actual trajectories along which the Run-time Navigation System guides the MDH
for collecting data from 60 node Uni network are shown in Figure 8-4. This figure bears
out the collection—time and round trip distance information shown in Figure 8-3 in a
visual form. It can be clearly seen that for D—MDO and CENT schemes, the trajectory is
shorter than D-PNT. For RW, the trajectory is completely independent of the node

positions and parts of the network are not visited at all.

 

 

 

     

         

   

 

 

 

 

 

 

 

 

 

 

 

100 — E D-PNT g CENT
D-MDO RW _
N Area-Scan Area
Scan
90 — , ..
8
8 E .. S a E RW
'5 ‘E E: 5i E E D'MDO
e E :- 5: a -:
a E E 5‘ E E
A5, 70 - E It
m 5 5 ;~ 5 CENT
°\° :-. A
60 —— E 5! ~~ E
5 a g: :: a D-PNT 100
50,. 55: E E o 20 4o 60 80 100
3-Tn’ 3-Line Uni Latt Collection Success Index

 

 

 

 

 

 

Figure 8-5: Packet delivery and Collection success index

In Figm'e 8-2, it can be noticed that random walk (RW) provides average delivery
delay comparable to D-MDO for all topologies, however at the expense of very low
packet delivery rates (Figure 8-5) and high delay variations. In Figure 8-5, a lower than
60% packet delivery ratio in most cases for RW is because while “random walking” the
MDH may collect data from the same nodes multiple times before visiting other nodes. In

fact, as seen in Figure 8-4, it may never visit few nodes, which explains the low

133

collection success index as depicted in Figure 8-5. In addition, we found that the delay
variation experienced by data packets for RW is large because of the large variation in
the successive visits to the sensor nodes. Due to the low coverage, packet delivery ratio
and high delay variation for data, RW is not suited for practical use in realistic sensor
networks.

The collection success index represents the percentage of nodes from which at least one
packet has been collected. Figure 8-5 shows the collection success index values for 3-Tri
topology and it indicates that RW has collected data (at least one packet) from about 73%
of the nodes while all the other strategies achieve close to 100%. The low collection
index also helps the RW mechanism to reduce the average delivery delay by shortening
the average duration between the MDH’s visits to a node, and also by reducing the
queuing delay at the visited nodes. Similar collection success index values were observed
for other topologies. Also, as shown in Figure 8-5, the delivery ratio achieved by CENT,
D-MDO and Area-Scan are significantly higher compared to RW. However, they are not
100% because few data packets remain uncollected in the node buffers when the

simulation is terminated.

8.3 Multi-hop Data Collection (k > 1)

This subsection reports the impacts of varying k on various network performance
metrics in a 60 node lattice sensor network (Latt) as shown in Figure 8-1. The value of k
was varied from 1 to 3. For the chosen topology, k=4 represents the static sink case.
Meaning, for this particular topology, there exits a topological location to place a static
sink so that each node can reach the sink with up to 4 hop routing. Note that D-PNT is not

included in this section because it does not apply to multi-hop data collection scenario.

134

 

 

 

‘40 ‘ ICENT
120 , ID—MDO , _
ElArea—Scan A“
1001 w .

  
  

\
I

 

 

 

Delivery Delay (sec

 

Value of k

 

 

 

Figure 8—6: Impact of k on average delivery delay

Results in Figure 8-6 indicates that the packet delivery delay progressively goes down
for protocols CENT, D-MDO and Area-Scan as the value of k increases. The scenario
with k=1 has the highest delivery delay as it represents the single-hop collection. For all
three mechanisms, as the value of k increases, the overlay construction process as
outlined in Phase 1 of Section 4 identifies smaller nmnber of Navigation Agents (and
hence Designated Gateways), and therefore the sink has to visit fewer nodes during each
collection cycle within the sensor field. This shrinkage of the sink trajectory length
reduces the trip collection-time too” as reported in Figure 8-7.

Since the packet delivery delay is a function of the trip collection—time, the delivery
delay in Figure 8-6 closely follows the trip collection-time trends in Figure 8-7. Note that
the trip collection-time is not meaningful for the Random Walk (RW) solution [106] since
the sink is almost guaranteed to not complete the round trip on a given collection cycle.

That is why it is not included in the trip collection-time chart in Figure 8-7. As expected,

135

for all k, the centralized protocol CENT chooses a smaller set of Navigation Agents than
the distributed D—MDO mechanism as outlined in Section 7.2. This explains CENT’s

shorter trajectory, trip collection—time and the packet delivery delay.

 

 

 

 

  
  

 

 

 

 

 

 

250 CENT 100
I D-MDO

1;; El Area-Scan
v 200 — I RW .5—3 80 —
a) m
E r_
3? E‘
g 150 ' 02) 60 _.
“8 r:
2 33
8 100 - g 40 —
a 8
-: Q
'— 50 ‘ o\° 2o -

0 ‘ 0 _

1 2 3 1 2
Value of k Value of k

 

 

 

 

 

 

 

 

Figure 8-7: Impact of k on collection time and delivery

In the case of Area-Scan, as the MDH moves, it sends out a k—hop beacon in response to
which all nodes within k-hop distance forward the data stored in their buffers to the sink.
Unlike for the CENT and the D—MDO mechanisms, no Designated Gateways (DG) are
used for data aggregation and upload to the sink. The increase in k leads to an increase in
the amount of data that needs to be simultaneously collected by the sink. This increase in
data causes MAC layer (802.11 has been used for these experiments) contentions and
subsequent packet drops, which, in turn, causes the delivery ratio for the Area-Scan to
degrade. This is evident in Figure 8-7. MAC contention does not pose such a problem for
D-MDO and CENT because for these mechanisms the sensor nodes route their data to
Designated Gateways (DG) where it is buffered prior to the arrival of the MDH. When

136

the sink arrives, the data is uploaded to the sink using single-hop transmissions only from
the DGs, as opposed to from all up to k-hop nodes as in the case of Area-Scan. This helps
the MAC layer drops to remain insensitive to k for CENT and D—MDO protocols. Unlike
other protocols, Random-Walk scheme experiences an increase in packet delivery delay
with increasing k. This is caused primarily due to its unpredictable sink trajectory,

although the packet delivery remains fairly insensitive to k.

 

 

 

 

 

 

 

 

 

   

2 12 CENT
a I D-MDO
1.6 - g 10 IArea-Scan
E “J I RW
: or
0 .s
E
(g 1.2 ~ 2
I at
a)
g 0.8 ~ “5
a: >'
E 8
0.4 " _C-
5
0 _
2 3 2
Value of k Value of k

 

 

 

 

 

 

 

 

Figure 8-8: Impact of k on hop count and remaining energy

As shown in Figure 8-8, with increasing k, the average hop count for a data packet
increases and hence the energy required to route it increases. The increase in multi-hop
routing leads to uneven energy drainage, which, in turn, increases the Standard Deviation
of Remaining Energy or SDRE metric as evidenced in Figure 8-8. The combined effect of
the increase in energy/packet and SDRE leads to a decrease in network lifetime. Results

in Figure 8-6 thru Figure 8-8 confirm the delay-lifetime tradeoff and its sensitivity to the

137

hop bound factor k, as postulated in Chapter 6. Parameter k can be used as a ‘knob’ to

achieve a balance between delay and lifetime.

 

 
  
 
     
    

 

 

 

 

 

 

 

 

 

 

 

 

(a) k= l
150 - 49 50 51
o o O
42 44
O o
59 37
0
100 ~
58
o
57
O
. 56
50 .
0 O
o 50 100 150
150 J 55
O
48
O
59 41
O O
100 -
58 34
O O
57 27
O O
- 56 20
50 o o
13
O
6
O O
o 50 100 150

Figure 8-9: MDH trajectories for varying k

138

Figure 8-9: MDH trajectories for varying k (contd.)

 

 

 

 

 

 

(c) k = 3
150 1 49 50 51 52 53 54 55
O O 0 O O O O
42 47 48
O o o
59 35 4o 41
O O O O
100 ~
58 28 33 34
O O O O
57 21 26 27
C O 0 O
50 - 56 14 19 20
O O O O
7 12 13
O O O
o 5 6
O O O
0 I l
o 50 100 150
0 Navigation Agent @ lnterrnediate Navigator

 

 

 

Figure 8-9 shows the D-MDO trajectory followed by MDH in the network for k = l, 2
and 3. These experimentally observed trajectories visually confirm that as k increases,
the number of NAs identified goes down which results in shorter trajectories and
subsequently lower trip collection-time. Multi-hop data collection experiments were
carried out on a number of additional topologies with different node counts, node
distributions, and densities. Results from those topologies showed similar patterns,
confirming the energy-delay tradeoff using hop-bound k as shown above.

8.3.1 Impact of Network Density

Experiments were also conducted with varying node density or node degree. With a

constant sensor field area of 150m X 150m, the number of nodes was varied to achieve

139

an average nodal degree of 6.5, 9.7 and 15. The results obtained are shown in Figure

 

 

 

 
 
   

 

 

 

 

 

 

 

 

 

 
 

 

  

 

     

 

    

 

 

 

 

 

 

 

 

 

 

 

 

8-10.
Average Node Degree = 6.5

100 - ECENT
’5; INGDC
% 80 - aArea-Scan
O
E 60
3'2 40 - .. :
E- 3'“
E 20 -
2 31:3: :3:
LU 0 - 4.3. ...

1 2 3 2
Value of k Value of k

. Average Node Degree = 9.7

200 ICENT 2° ‘ DCENT
g? l INGDC INGDC
g 160 ‘ EnArea-Scan _“ 15 - EIArea-Scan
E 1 m
r: n: 10 ————
x D
8 co
5 5 -
o
C r
m _

1 2
Value of k

300 l ECENT a

w 250 - 'NGDC INGDC

 

EiArea-Scan

 

El Area-Scan
I

 

 

 

 

 

1 2 3
Value of k

Value of k

 

 

 

 

 

Figure 8-10: Impact of node density on energy metrics

The results for D-MDO indicate that with increasing density for a given value of k, the

absolute values for the energy metrics AEPP and SDRE goes up. This indicates that the

140

on the basis of the model analysis. Thus, for regions of different densities, it is clear that

different values of k must be chosen to ensure that overall lifetime objective is optimized.

8.4 Performance Summary

The performance results lead us to conclude the following about the proposed NADC
framework: 1) it reduces collection-time and hence delivery delay compared to other
strategies while maintaining complete coverage and high delivery ratio 2) Direction
information can be used for MDH navigation in the absence of location information 3)
The tunable parameter k can be used as a knob to achieve a desired balance between

delay and network life.

8.5 Summary and Conclusions

In Chapters 6 thru 8, a generalized non-real-time data collection framework for
jointly determining the MDH trajectory and routing was developed. Analysis of the
model for the framework established some bounds for the hop bound factor k. We
explored the idea of using a network assisted sink navigation and data collection
framework to perform k-hop sensor data collection without node localization services.
We proposed a generalized solution framework and developed distributed algorithms
which allow the sensor network to 1) identify a subset of nodes as sink navigation agent
and data collection points, 2) compute a sink navigation path using ANT based
distributed TSP solution, 3) identify Designated Gateways (DOS) and routing paths from
sensors to DOS, and 4) dynamically navigate a sink by the navigation agents along the
computed path. An N82 based simulation model was developed to evaluate the proposed
protocol and to compare it with a munber of other data collection mechanisms including

one with centrally computed navigation paths. Experimental results indicate that the

141

preposed distributed mechanism achieves better performance than previously used

distributed strategies and comparable to centralized solutions.

142

Chapter 9. Stability Analysis of Multi-hop Data Collection

9.1 Motivation

In Chapter 6, a model was developed based on the hop-bound factor k to represent
different data collection scenarios, namely, single-hop with mobile sink, multi-hop with
static sink, and different levels of mobile-sink-based multi-hop routing in between. In this
chapter, this model is used as a means to study the broader impacts of multi-hop routing
on data collection delay performance. The basic goal is to investigate if any arbitrary
amount of multi-hop routing can be used in the context of mobile sink deployments. In
the process, a number of thresholds of k that impact stability and acceptable performance
will be derived from the model. Finally, the system performance trends obtained from the

model are validated using a packet level simulation in n32.

9.2 Routing Thresholds
9.2.1 Stop threshold

The MDH collects data from all Designated Gateways once every collection cycle (1).
Hence, during each cycle, all generated data during the most recent collection cycle needs

to be uploaded to the MDH. Thus, Tu , the time required to upload the data that has been
generated since the MDH’s previous visit, can be expressed as (tiny C . If Tu 3 TP ,

the MDH can collect all generated data without stopping at any DG Therefore, the lowest

value of k for which stops are required is referred to as the stop thresholdk The

stop '

condition for obtaining the stop threshold is:

 

 

1,1de 3L
(r)/ C > 4 -
C (2km, + 1)v

143

Now substituting the value of L from Eq. 6-1, we get:

2
3 ”R +2R
(fiflde)> [ Akstop m]

 

‘ ‘ ...... (Eq. 9-1)
C (sttop + I)»
Simplifying the equation, we obtain:
2 Zrlndev 72.7212de
clkstop +C‘sttop +63 > O , where cl = T 9 62 = T _ 2Rm and
7&2
63 = “7

After solving the above equation for ksmp , the positive root is considered as the stop
threshold kstop' If k is larger than this stop threshold, the mobile sink is required to stop

around one or multiple DGs in order to collect all accumulated data since its last visit.

Because of these mandatory stops, Collect-on-Move operation is not feasible for values of

k beyond kmp. Note that there is no distinction between the stop thresholds for the

Stop-on-Demand and Collect-on-Move modes. This implies that if the value of k is above
this stop threshold, the MDH has to be capable of reducing its speed, stopping or

executing a holding pattern.

The stop threshold kstop can be particularly significant in choosing the appropriate

MDH technology for a given application. For example, since the fixed-winged UAVs are
not able to stop or hover, the deployed amount of multi-hop routing has to be below the

identified threshold k stop . Alternatively, if a higher extent of multi-hop routing is needed

from other application and system constraints such as delay and sensor network life, then a

144

rotary-winged UAV technology will have to be used for satisfying the hover/stop
requirements.
9.2.2 Stability threshold

As mentioned in Section 6.2, the system is stable only when the trip collection-time is

smaller than the collection cycle, that is ta,” S r . From Eq. 6-4, the condition can be
written as Tm s r or Tm + (Tu — TP )5 2' depending on the value of the stop component.
In either case, as k increases, the quantities T m and T 1, decrease as seen in Eq. 6-2

and 6-4. The lowest value of k that allows the trip collection-time to be within the

collection cycle I is referred to as the stability threshold k stability .
Stop-on-Demand: Since in this case the stop component of the collection time is non-zero,

the condition for stability is Tm + (Tu — T

p )5 z' . Substituting the values of Tm ,

Tp and Tu , we can write:

2 2
IIR +2R 3 ”R/ +2R
[ flkstability r J m + 12%de _ [ sttabilityr m
v

 

 

 

 

. . S 2' ,
C v(2kstability +1)
Upon simplification, we get: Clksztability + Czkstability + c3 S 0 , where
2R lmRZd ӣ2 6Rm mafia m2
c1=2 '"+ —z' , 6'2: — + -T , and C3=-— .
v C rv v C rv

Similar to the stop threshold, solving this equation and choosing the positive root
provides the k-threshold for stable operation.
Collect-on-Move: In this case, since the stop component of the collection time is zero, the

stability condition can be written as Tm s r , or

145

2
”R + 2R
{ Astability r] m

V

 

<r.

IZRZ
2r(vr - Rm) .

 

SOIVing for kstability a we get kStability 2

The results above indicate that certain degree of multi-hop routing can be essential in
certain applications to ensure operational stability of a mobile sink in both
Stop-on-Demand and Collect-on-Move modes. Note that this analysis assumes that the
nodes have infinite buffers and therefore no packets are lost due to buffer overflow. The
instability is defined only in terms of when the trip collection-time exceeds the collection
cycle 1. However, when nodes have limited buffers, instability would also manifest in the

form of unacceptable packet loss rates.

9.2.2.1 Bound for Data Generation Rate
Consider the stability condition: Tm + (Tu — Tp )s z' . When the upload
time Tu dominates both Tu and T p , then Tu s r or (r x ,1 x n)/ C S r. This gives a bound

,1 S C/n for the data generation rate from each node. Meaning, if 1 becomes greater
than C/n, the data rate is not sustainable and the system will not be stable. This is

intuitive because the aggregated data generation rate of the entire network has to be less

than the rate C at which the MDH can collect data.

146

<

Unstable

coll (S)

4—0

Stable

 

r

<

Unstable

tool! (5)

>

Stable

 

 

     
  

 

 

 

 

 

 

  
  
 

 

 

 

 

 

 

 

 

3000 - Stop-on-Demand()\=8 bps)
2500 r
2000 ‘
1500 _ i stability-threshold 3 [stop-threshold1
I
500 ~ 1°9\‘l
IRan es’gab’e kl
O . I I M...“ .l .2 ,. H. _i. l l '
0 1 2 3 4 5 6 7 8 9 1O
Hop-bound (k)
(a)
3000 _ Stop-on-Demand(A=8 bps)
2500 r
2000 '
1500 _ : stability~threshold l i stop-threshold j
1000 ‘ [T900
I
500 - ‘\
l"Van 98],:bl9 kl
0 I l l . ... -.... ', . -l..... ,.l 1
0 1 2 3 4 5 6 7 8 9 10
Hop-bound (k)
(b)

Figure 9-1: Impact of hop-bound k on trip collection-time, t co” in case of

Stop-on-Demand collection

9.3 Numerical Evaluation of the Collection Time

Stop-on-Demand: Figure 9-1

shows the trip collection-time for the

tcoll

147

Stop-on-Demand mode with varying k. Figure 9-1:a and Figure 9-l:b report the results

for two different values of it. For all k, with increasing k, tea ,1 decreases monotonically,

initially at a fast rate. With higher 1, it decreases at a lower rate. To understand this

behavior, in Figure 9-2 we plot the quantities Tm , Tp and T u . The move time and the

productive move time Tm and T p are dependent on k but not on k. The upload time T u ,
on the other hand, depends on it but does not vary with k. It is evident that with increasing
k, initially the quantity Tm decreases quite sharply, and then the rate of decrease reduces
until k reaches akm-n-ca, , which represents the static scenario as shown in Figure 6-1:a.

This is because with increasing k, the sink trajectory includes fewer and fewer Navigation

Agents and hence it becomes shorter.

    

 

 

 

 

3000 -
2500 ~
78‘
3 2000 - TD
3
". 1500 -
O.
[_-
§ 1000 ~ 1:900s
l- I.-. -._-;.:;:+- a g i.
500 _Tu (k=56 bps)
Tu(}\=8‘bps) . A
o _W;._,__._-;__ :—
012 3 4 5 6 7 8 910

Hop-bound (k)
Figure 9-2: Impact of hop-bound k on T m , T p and T u

The quantity T p goes down at a much faster rate because as the Navigation Agents
are spread farther apart, the MDH is not in contact with any DG during an increasing

148

fraction in its trajectory. For all It, as k increases, the collection-time goes down fast until

the stop threshold k

 

is reached, and then with a slower rate until ken-“ca, is reached.

  
  

 

 

 

 

 

  
 

 

stop
F 3000 ' Collect-on-Move()\=8 bps)
2500 -
2
8
‘2' < A 2000 -
2) u:
:8 1500 ‘
x 1000 --.Ti?,°9.-,
9 I
g 500 - N
:5! es]
(0 I Rangreable k |
0 I I I .......=' I I I I I. "I
0 1 2 3 4 5 6 7 8 9 10
Hop-bound (k)
(a)
F 3000 - Collect-on-Move()\=56 bps)
2500 -
2 ib‘WT‘Wm_TT’l
% stop-threshold l
:7, < 2000 - ‘”‘““‘”“‘““”"‘J
c A
3 3
3 1500 -
I
x 1000 459.99 .. -
9
g 500 —
a)
0 I I ' f fi' I I I I I I
0 1 2 3 4 5 6 7 8 9 10
Hop-bound. (k)
(b)

Figure 9-3: Impact

of hop-bound k on tco” in case of Collect-on-Move collection

149

As seen in Figure 9-1, tea” is initially dominated by Tm, but as Tp drops below

Tu (see Figure 9-2), the condition t can > Tm (see Eq. 7-4) holds. At k = k

critical ’ t coll

is the same as Tu . For the xi =8 bps case in Figure 9-l:a, the values of k < 4 define

the region of instability as the trip collection-time becomes greater than t. For k < 6,

the MDH can collect data without stopping because T p > Tu , and therefore kstop = 6.

Fork > 6 , the MDH has to stop at certain DGs for stable data collection. In contrast, for

21:56 bps in Figure 9-l:b, the stop threshold km,p =2, and the stability threshold

kstability = 9. Thus, in this case the stop threshold occurs before the stability threshold.

Collect-on-Move: Figure 9-3 shows the impacts of varying k on trip collection-time for

different data generation rates. In this case, the trip collection-time tcoll is equal to the
move time, Tm as shown in Figure 9-2. In Figure 9-3, the stop threshold is identified for

both scenarios xi = 8 bps and 56 bps. In this mode, the stop threshold as discussed in

Section 9.2 defines the upper bound on the extent of multi-hop routing for stable

operation. Hence, the values of k beyond k (k > 6 for 7t = 8 bps and k > 2 for k=56

stop

bps) are not shown in Figure 9—3. For i=8 , the desirable range of k is

k 2 ksmbimy andk < ksmp. As shown in Figure 9-3zb, in the case of k = 56 bps, no value

of k supports stable Collect-on-Move operation. This is because values of k < 4 lead to

unacceptably long trajectories while values k > 2 require stops.

Based on the analysis thus far, the following inferences can be drawn.

a) For given network parameters, there is a lower-bound of k (kmbmty) for stable

data collection which implies that single hop collection may not always be

150

b)

d)

g)

feasible.

There exists a threshold kmp which impacts the choice of the MDH

technology. For example, since the fixed-winged UAVs are not able to stop or
hover, the deployed amount of multi-hop routing has to be below the identified

threshold k mp. Alternatively, if a higher extent of multi-hop routing is needed

based on other application and system requirements such as delay and network
life, then a rotary-winged UAV technology will have to be used for satisfying
the hover/ stop requirements.

In Collect-on-Move, the range of k (i.e. the extent of multi-hop routing) that

allows stable operation is limited compared to Stop-on-Demand.

In applications where it is required to use value of k < kstabih-O, or k > kmp ,

multiple sinks need to be deployed for stable collections.

There is no specific relationship between the thresholds k “ah-W and k stop .

If xi. is low enough to allow kstability < kmp , then it is desirable to pick a k in

between them to provide stable operation without the MDH having to stop.

The reduction in tea” beyond kmp is small since stops have to be

incorporated, but higher energy cost is incurred due to increased multi-hop
routing.

MDH trajectory should be computed such that T p z Tm to push k mp as high

as possible, and thereby increase the desirable operating range for

Collect-on-Move.

151

9.4 Properties of the Stop and Stability Thresholds

 
  
 
  

 

 

1O -
A 9 '
£5,
fl 8 ‘ 6"" kstability
% 7 _ (Stop—on-Demand)
E 6 '
'o 5 A
S
o 4 ‘
.0 WI
61 3 r
a l

2 kstability

1 - (Collect-on-Move)
0 I I T I r I
0 20 40 60 80 100 120

A(bps)

Figure 9-4: Impacts of data rate on stop and stability thresholds

Stop-on-Demand: As seen in Figure 9-1, the rate at which nodes generate data relative to
the MDH-DG contact period can impact the stop and stability thresholds. Figure 9-4

shows the trends of kstability and ksmp with increasing data rate 2.. As it increases,
kstability increases due to the higher data upload times (as seen in Figure 9-1:b) which

result in higher too”. Hence, in order to maintain stability, it takes a higher value of k for

the trip collection-time to be lower than the collection cycle I. This implies that as the
data rate goes up, the amount of multi-hop routing that is required to allow stable data
collection also goes up.

The stop threshold, on the other hand, experiences a downward trend with increasing
9». This is because, the value of k for which the mobile sink has to stop and collect data

decreases due to the increasing upload time. Thus, for sufficiently high data rates, even

152

single hop data collection (k = l ) can require the sink to stop.

 

 

8 _
3? 7 -
T8“ kstability (Stop-on-Demand)
‘5 5 ‘
5
92 5 ‘
[E 4 - kstop
'U
S \
o 3 - .—
‘T \
O.
o 2 q _.—_
I \
1 * kstabmty (Collect-on-Move) —————
0 r I I I I I I I
0 2 4 6 8 10 12 14 16
MS Speed (m/s)

Figure 9-5: Impacts of sink speed on routing thresholds

Figure 9-5 reports the impacts of sink speed on the thresholds. As the sink speed
increases, the DG-MDH contact times decrease, necessitating sink stops. This is why the
stop threshold decreases. At the same time, since the mobile sink moves faster, the

move-time Tm decreases. This effect reduces tco” and enables lower and lower values

of k to achieve trip collection—times below the collection cycle I, pushing the stability
threshold downwards. Thus, a faster moving sink will be able to support stable data
collection with lesser degree of multi-hop routing. However, it will more likely to need
the stopping/hovering capability at a relatively lower data rate.

The impact of node density is shown in Figure 9-6, in which the trend resembles the
data rate scenario (in Figure 9-4) to some extent. The stop threshold goes down as the

network density increases because more nodes and hence more packets are buffered at

153

the DGs. The stability threshold goes up as longer stops become essential to collect the

     
  

 

 

 

 

buffered data.
8 - 4r
3 7~
:8 6 _ kstability
_g (Stop-on-Demand)
(D 5 d
9
Is 4-
'0
g 3 - kstability
_8 (Collect— on-Move)
3:. 2 -
:3
1 ..
o I I \ j I I
0 1 2 3 4 5
Nodes/1000m2

Figure 9-6: Impacts of node density on routing thresholds

Collect-on-Move: As mentioned in Section 6.3, for a Collect-on-Move sink, the
trip-collection time is equal to its travel time. As seen in Figure 9-3, with increasing data
rate, the stop threshold moves down. Since the values of k above the stop threshold are not

stable, the range of k usable in this mode is restricted to k < k mp and k 2 k stability . It

can be observed that the range of k for stable operation continually shrinks with increasing
data rate until it reaches zero. As shown in Figure 9—6 similar logic holds when the node
density is increased.

Similar to the Stop-on-Demand case, with increasing speed, the sink move time reduces

and hence the k stability progressively goes down. The threshold k mp goes down as well

due to decreasing contact time with the DOS as mentioned earlier.

154

9.4.1 Applicability to Non-circular Terrains

Although the stop and stability thresholds (i.e. km}, and k “am-O, ) have so far been

determined and analyzed for a circular network terrain, the analysis in Sections 9.2.1 and
9.2.2 can be also applied to other non-circular terrains as long as the trajectory length, L

and its resulting move time Tm are monotonically decreasing functions of k (see Figure

9-1 and Figure 9—2). Regardless of the terrain shape, by definition, as the hop-bound
factor k increases, the amount of data that needs to be uploaded via each Designated
Gateway (DG) increases. That means, for a given per-sensor data generation rate, the
number of needed DGs decreases. Since DGs are chosen to be the l-hop neighbors of the
Navigation Agents, reducing the number of DGs gives rise to fewer needed Navigation

Agents. The end result is a shorter trajectory L, and the resulting move time Tm.
Therefore, the condition is true that both L and Tm reduce monotonically with increasing

hop-bound factor k, confirming that the analysis in Sections 9.2.1 and 9.2.2 will hold for
general terrain shapes in the presence of hop-bounded routing as defined in this thesis.

To validate the existence of the stop and stability thresholds for other terrain shapes, we
repeated the related analysis with a square shaped terrain. We consider a square region
whose side is A meters long. A trajectory consisting of concentric squares can be
constructed similar to the one for circular terrain as shown in Figure 6-5. The trajectory

length L is computed as:

J—
L = 4A/2 2L]:— (s/Ekrai -1))]+ 2R»: .......... (6)

i=1

155

p 1400 ‘ Stop-on-Demand()\=8bp$)

 

1
l

i stability-threshold

 

 

Unstable
8
O
O

 

 

 

Stable

 

 

 

 

Hop-bound (k)

  

 

 

 
 
  

 

 

 

 

    
 
  

  
  

 

 

 

   

(a)
F 1400 - Stop-on-Demand()\=56 bps)
1200 ‘ Tm
.30 stop-threshold
% < 1000 - Tp
: A \ i stability-threshold }
3 w _
v 800
’ I: 700s
..8 600 WI _
Desirable k
f 400 ‘ Range ‘1': |
g I l
9 fi 200 ~
In
K o i MIT ”T i - l ”I. . "M‘Uv—ILHUU
0 1 2 3 4 5 6 7
Hop-bound (k)
(b)

Figure 9-7: Impacts of k on ta,” for square network region

We evaluated the trip collection-time for square network terrain with side =500m,

transmission range =50m and r =700s. For this network ken-“ca, =7. The trip

collection-time results obtained for different data generation rates for Stop-on—Demand

156

data collection are shown in Figure 9-7. It can be observed that the results and their

patterns are very similar to those shown in Figure 9-1 for the circular terrain.

9.5 SIMULATION EXPERIMENTAL RESULTS

9.5.1 Impacts of Data Rate

In Section 9.4, it was observed that the data generation rate of the nodes has impacts on
the stop and stability thresholds. In addition, increasing data rate can affect the data
collection performance due to higher MAC layer contention and the resulting collisions.
We conducted data collection experiments on a l44-node network with nodes arranged in
the form of a regular square lattice. With this network size and the chosen wireless

transmission range, the hop-bound factor k can be varied from 1 through 5 for the mobile

sink scenario. The case for k = 6 represents the static sink scenario indicating

that kcritical = 6 '

 

% Packet Delivery Ratio

 

 

0 I I I I I
0 1000 2000 3000 4000 5000
A(bps)

Figure 9-8: Packet delivery performance for varying hop-bound factors (k)

157

600 -

 

 

 

 

 

 

 

 

 

 

     

 

 

I MAC-Queue k- 2
500 _ DRouting Layer Buffer _ I
‘g DDelivered
5 400 -
*3 -
3
8 300 - _
‘6
x _
8 200 — __ _
100 .
0 H ; {—1 I W I ' l ' l V I I I l . ‘ 1
128 205 512 1024 1280 1463 1707 2048 2560 3413 5120
Mbps)
(3)
600 -
I MAC-Queue k= 5
500 — URouting Layer Buffer

El Delivered

Packet Counts (1 O3)
0)
O
O

 

 

MIA
Omufi: 1".I x13?! ,1 I '7'

128 205 512 1024 1280 1463 1707 2048 2560 3413 5120
Mbps)

(1))

Figure 9-9: Delivery and drop performance for varying data rate when (a) k=1, and (b)
k=5

Figure 9-8 shows the packet delivery ratio performance with varying data rate (1.) for
different k values. In order to analyze the delivery results, we collected packet level

statistics which include the number of packets:

158

a) delivered to the mobile sink
b) buffered at the routing layer in the DOS
0) dropped by the Data Link Control (DLC) layer interface queue
The following observations can be made from the results. First, the packet delivery
performance is lower in general for higher k for all 1. values. The number of delivered
packets in Figure 9-9 corroborates this effect. At the end of the simulation, packets
remain in the routing layer buffer waiting to be uploaded to the MDH during the next
collection cycle. At lower data rates, higher the value of k, larger the amount of data
accumulated at the buffers at the end as seen in Figure 9-9. At higher data rates, higher k
leads to higher drops fiom the DLC interface queue because of MAC collisions and also
insufficient contact duration with the MDH. Second, delivery ratio in general goes down
significantly after a certain data rate. Higher the value of k, the precipitous decline in
delivery ratio occurs sooner. For instance, the decline occurs at k=2048, 1280 and 128
bps for k=1, 3 and 5 respectively. This can also be explained by higher collisions and
drops due to limited contact duration with the MDH, as seen in Figure 9-9.
9.5.2 Thresholds for Hop-bound (k)
We conducted experiments the l44-node network with nodes arranged in the form of a
regular square lattice mentioned earlier. The trip collection-time obtained for varying k is
shown in Figure 9-10. As predicted by the model in Figure 6-7 and seen in Figure 8-3 for

a 60-node network, we can observe that as the hop-bound increases, ta,” goes down

due to the reduction in trajectory length. Figure 9-11 depicts the variation of the stability

threshold kmbih-ty with increasing collection cycle I. Since the stability threshold

k stability indicates the minimum amount of multi-hop routing that allows system stability

159

(fool! <1), as 1: decreases, the amount of multi-hop routing that is required for stability

goes up. In other words, higher the degree of multi-hop routing (i.e. higher k), lower the

collection cycle duration that can be supported by the system.

600 -
500 -

400 -

tcoll (S)

300 -

200 ~

100 ‘

 

o .4
1 2 3 4 5
Hop-bou nd (k)

Figure 9-10: Variation of Trip collection-time

kstability

 

 

500 450 400 350 300 250 200 150 100

Collection Cycle.r (in see)

Figure 9-11: Variation of stability threshold

160

Now, we explore the impact of data generation rate on the thresholds. As observed in

Section 9.4, while the stability threshold ksmbih-ty is expected to be insensitive to the

data generation rate k for Collect-on-Move mode, the stop threshold k is not.

stop

Guided by this observation, we experimentally investigate the impacts of A on k In

stop -

order to determine the stop threshold ks“,p for different data rates, it is necessary to

determine the k at which the MDH-DG contact duration becomes insufficient to collect
data buffered at the DGs.

To find that threshold value of k experimentally, it is important to identify scenarios in
which the sink moves out of the range of a node when it still has packets in its wireless

network interface queue. Such events happen when the DG-MDH contact duration is

insufficient (i.e. the value of k is already larger than ks“,p for the given network

parameters), as a result of which the interface queue starts dropping packets after the
MDH leaves prematurely. Stop thresholds identified using this experimental mechanism
are reported in Figure 9-12.

Figure 9-12 and Figure 9-13 show the impacts of data generation rate and sink speed on
the experimentally found stop threshold. The MDH speed was varied from 3 m/s (typical
speed of mobile sensor robots [83]) to 25 m/s (typical speed of mini-UAVs [150]).In
Figure 9-12, it can be observed that for a given sink speed, the stop threshold
progressively go down as the data rate increases. This behavior is in agreement with the

trend of km,p as found via the analytical model in Section 9.5.1 and Figure 9-4. As the

data rate increases, the amount of data buffered at the DOS increases implying that the

sink would need to stop at lower values of k. For a given xi , we can also observe that the

161

stop thresholds are lower for higher speeds. This can be attributed to the fact that

increasing speed results in shorter contact durations between the MDH and the DGs.

kstop

Hop-bound Thresholds (k)

    
  

speed=3mls

    

speed=5mls

     

 

 

 

1 I I f I I

0 300 600 900 1200 1500 1800
A(bps)

Figure 9-12: Impact of data rate on the stop threshold

kstop (A = 85 bps)

fl I

 

\ kstop (A = 102 bps)

A A A A g
I n 7 - i -

 

 

 

kstabimy
0 5 10 15 20 25
MDH Speed (m/s)

Figure 9-13: Impact of speed on the stop threshold

162

The stability threshold is not affected by the changing data rate, but it is sensitive to
speed as shown in Figure 9-13. As the speed increases, the stability threshold moves
down because the sink move time T m progressively reduces. For example, with a sink
speed of lm/s, while all k 2 5 represent stable operation, for 2m/s, all k 2 3 are stable.
For speeds 3m/s and higher, all values of k are stable. Figure 9-13 also shows the impacts

of sink speed for different values of 2. on kstop. As the sink speed increases, the stop

threshold moves down for reasons mentioned above. The higher the data generation rate,
the lower the stop threshold. This behavior also matches the trends found through the

analytical model in Section 9.4 and Figure 9-5.

 
    

 

 

6-
E km, (A=68 bps)
s 5-
'5
.C
4.
,5
‘8 3“ /
3
O
9 2-
g»; kg... (A = 512 bps)
1-
0 I I T T I l
2 3 4 5 6 7 8

Nodes/1000m2
Figure 9-14: Impact of node density on stop threshold

Figure 9-14 shows the impacts of node density on the stop threshold at different data

generation rates with a sink speed of 3m/s. At this speed, the value of kmbiluy turns out

to be 1, which corresponds to single hop data collection. This also means that all possible
values of k can offer stable operation. As predicted from the model (see Figure 9-6) it was

163

experimentally found that the node density does not affect the stability threshold for the
Collect-on-Move operating mode. The stop threshold, on the other hand, reduces (see
Figure 9-14) with increasing density. This trend matches with what was observed from
the model in Figure 9-6. For a given data rate, with increased node density, a sink has to
collect data from an increased number of DGs around each Navigation Agent. Hence the
amount of data to be collected within the DG-MDH contact duration increases, requiring
stops at lower levels of multi-hop routing. This brings the stop threshold down. For a

given density, higher the data rate, the lower the stop threshold as shown in Figure 9-14.

9.5.3 Guidelines for Choosing Hop-bound Factor k
The analysis presented above shows a tradeoff between energy consumption and data
collection-time for the variable amount of multi-hop routing represented by the
parameterk . Based on this observation, the following recommendations for choosing k
can be made.
1) The sink technology dictates the choice of k. For sink devices that are incapable

of stopping, k has to be smaller than the stop threshold ks For sinks that can

top ‘

stop, k > ks can be chosen.

top

2) For a given collection cycle t, k should be chosen such that k 2 kstabmty.

3) If the sensor application has an upper bound on tolerable delay, then the minimum

value of k (subjected to the conditions k 2 kstability and k S km}, irrespective

of the relative values of k and kmp ), that meets the delay requirement

stability

should be chosen. This ensures that while meeting the delay requirement, the

network life is elongated as much as possible.

164

4) An energy-constrained MDH such as UAV can typically move for a limited
duration (say a) before it needs to be recharged or refiieled. For a given 1', the

minimum value of k that results in ta,” < a should be chosen in such

situations. In a hostile terrain, a UAV style mobile sink may also have a required
upper bound of the exposure time during data collection. The same k selection
strategy could be used with a defined here as the maximum allowable exposure
time.

5) MDH trajectory should be computed such that T p z T m to push k5“,p as high

as possible, and thereby to increase the desirable operating range.

9.6 Summary and Conclusions

In this chapter, the limits on the extent of multi-hop routing that can employed in the
context of mobile sink data collection were investigated. To this end, hop-bound k
thresholds referred to as the stop threshold and stability threshold were derived. The st0p
threshold represents the k beyond which the MDH has to switch to Stop-on-Demand
mode. The stability threshold, on the other hand represents the minimum value of k that
results in a trip collection-time that satisfies the collection cycle (I) constraint. These
thresholds are used to analyze the working of Collect-on-Move and Stop-on-Demand
collection modes from a stability standpoint. As a result, the feasible region of operation

for the hop-bound factor between 1 and ken-”ca, are identified. The impact of key

network and MDH parameters such as data generation rate, density and MDH speed are
investigated. The result trends that were obtained from the model are validated with
simulation experiments. Based on the insight gained from the analysis, guidelines for

choosing the value of k were identified.

165

Analytical and experimental results presented in this chapter prove that there are clear
limits to the extent of multi-hop routing (k) that can be used in a network. In other
words, single hop data collection using a mobile sink (k =1) or static sink data collection
may not always be feasible. The feasible range of k is affected by a number of network
and MDH parameters such as the data generation rate, node density, collection cycle
duration, MDH speed and the data collection mode. For Collect-on-Move mode, the
possible feasible range scenarios are shown in Figure 9-15. The scenarios are obtained by
varying the data rate, but the same holds good when other parameters are varied. For low

data rate, when kswp > kstabih-ty, the range is ksmbm-ty to ks On the other hand,

top °

when k stop < kstability , there is no value of k that is conducive to stable operation.

 
  
  

 

 

1 \ I \
Low A High A
kstop
§ ks‘abi’i‘y § kstability

'H n- — — — — —————————— b ‘H p- - — — — —————————— p
Feasible
Range

A \ - \

w I v I

k k

 

 

Figure 9-15: Feasible range for Collect-on-Move mode

Similarly, Figure 9-16 shows the feasible range possibilities for Stop—on-Demand.

Regardless of the data rate and the relative positions of kstability and ksmp , the feasible

range iS kstability t0 kcritical '

166

     
 

  

 

 

 

 

 

/\ /\
Low A High A
_ _ stop
'5 T:
o o
"" kstability "" kstability
.- -— — — -— —————————— D b- — — -- — ———————————— p
tical Feasible kcflm,
Range } Range
‘ X - \
‘ 7 ' I
k k

Figure 9-16: Feasible range for Stop-on-Demand mode

Thus, for a given set of application parameters, the data collection mode dictates the
feasible amount of multi-hop routing. The mode, in turn directly depends on the choice
of mobile sink technology (ability of sink to stop or hover) and vice versa.
Experimental results show that the choice of k outside the feasible region can lead to poor
data collection performance. Therefore, appropriate choice of k is very important to

ensure stable and high delivery data collection.

167

Chapter 10. Capacity-Aware Stability Analysis

10.1 Motivation

The generalized formulation of the data collection problem presented in Chapter 7 and
the stability analysis performed in Chapter 9 apply to networks where the data rate is low
compared to the capacity of the MDH-DG links. Results presented in Figure 9-8 and
Figure 9-9 show that as the data rate increases, the delivery and drop performance
deteriorates. Also, as the data rate increases, lower values of k become more appropriate
to maintain reasonable delivery performance. Also, the trajectory planning mechanisms
proposed in the existing literature consider the sink-sensor link capacity constraint which
plays a significant role in determining the data collection performance in data intensive
applications. In the light of insights gained thus far, the goal of this chapter is to extend
the model to accommodate the capacity constraint into account and study the impact on the
collection performance and the thresholds identified in Chapter 9.

10.1.1 Feasibility Range for Stable Operation

As explained in Section 6.2, in order for the system to be stable, the trip collection-time

teal, should be less than or equal to the collection cycle 1'. Since tea” is the cumulative

time required for the MDH to physically move along the trajectory and duration of all the
necessary stops, the system could be unstable in any of the following situations. First,
when the length of the trajectory is so high that the time required to complete it is higher
than 1:. Now, as explained in Section 6.6.2.1, the trajectory length is inversely proportional
to k. Therefore, the value of k below which the MDH traversal time over the trajectory is

greater than 1, marks the lower bound of the feasible range of k, which is denoted

as k5,_10w. At low data rates, this corresponds to k stability discussed in Chapter 6.

168

 

Non-real-time or
real-time

A A
{ Notusable \ \
l/‘K r'JLfil I

Strictly Non-real-time

 

 

 

 

' T 1“ | l
1 l l kcritical D
fi- Feasible —),
I | k ——-———)
kst._Iow kst__ high

 

 

 

 

 

 

 

 

 

Figure 10-1: Feasible range within non-real-time collection

Second, when the MDH needs to stop at the D65 for so long that the overall trip
collection-time is larger than 1:. As k increases, for a given data rate, the MDH needs to
collect more data from each DG. Therefore, for a given wireless link capacity, the MDH

has to stop at DGs, leading to higher ’coll values. The value of k at which tcoll
becomes higher than 1 due to those high stop durations (caused by high data upload times)
marks the upper bound for the feasible range of k, which is denoted by kst__high- For
given network density, data origination rate, MDH-DG link capacity and the MDH speed,
the problem of determining the feasible range of k within the non-real-time operating
region is equivalent to identifying ks,_ low and ks,_ high as shown in Figure 10-1.

10.2 Theoretical Analysis of Feasibility Range

10.2.1 Modifications to the Model

Data Upload Time:

As seen in Section 9.2.1, the upload time Tu depends on the capacity of the link (C )

between the MDH and the DGs. However, the total link capacity, C is not available for

169

the DOS to upload data to the mobile sink due to the interference caused by transmissions

in the neighborhood of the DOS. Thus, the upload time is dependent on the effective

capacity of the link between the DOS and the MDH referred to as C eff and Cefir < C.

The upload time is equal to the amount of data generated in the network since the

previous visit by the MDH divided by the effective capacity of the MDH-DG links.

2
Thus, Tu=(rxlx%efl= (271m? %fl_

The effective capacity of the MDH-DG link can be expressed as the total link capacity

minus the link capacity lost due to interference namely Cm. Thus C eff = C — Ci" . If a

separate channel is used for MDH-DG communication, then the transmissions around the

DOS do not interfere with the MDH data collection and hence the entire capacity of the

channel is available for uploading data resulting in Ceff = C. This provides an upper

bound for the data collection performance that can be achieved by the mobile sink. On the
other hand, if the channel used by the mobile sink for data collection is same as the channel
used for communication between the sensors, then the extent of interference. is dependent
on the data rate of the nodes, the node density and the hop-bound factor k. When the
MDH is visiting a Navigation Agent and collecting data from the DOS, interference is
caused by the nodes up to two hops away from the MDH sending data to the DOS. The

worst case for interference or C in occurs when all the data generated in the network gets
forwarded to the DOS. Ci” can be computed as the contact time of the MDH with the DOS

around an Navigation Agent multiplied by the amount of data (bps) forwarded by
interfering nodes. The contact distance with at least one DG in the vicinity of a Navigation

Agent can range from 2r (when a Navigation Agent and all its DGs are collocated) to 4r

170

(when at least two DGs of a Navigation Agent are located on two diametrically opposite
points on the circle of radius r around the Navigation Agent) leading to an average of 3r.
The data forwarded by the interfering nodes is essentially all the data generated in the

network except the data generated by the DOS themselves. Thus, we have:

Cm = 3% [”(k'Y/w — 7” 2M 1: 371914162 -1)v/ .............. (Eq. 10-1).

From Eq. lO-l, it is clear that the amount of interference increases as a function of
increasing data rate, node density or k for a given transmission range and MDH speed.
Substituting the value of CM , the upload time is given by:

(r/lrrR 2 dv)

T“ = (Cv — 3m3d,t(k2 — 1))

 

....... (Eq. 10-2)

10.2.2 Stable Operating Range

As mentioned in Section 6.2, the system is stable only when the trip collection-time is

smaller than the collection cycle or tea” S r . Thus, in the case of Stop—on-Demand

collection, Eq. 7-5 implies that T m + (Tu — T p )5 1. Therefore,

2
L + 1 117:]? vd ,___3£’__ < 1(Eq. 10-3)

[7 (Cv—3m3/rd(k2—1)) (2k+1)v _

 

Eq. 10-3 and Eq. 6-1 indicate that as k goes up, the trajectory length L goes down and

therefore, Tm and T p (i.e. first and the third terms in Eq. 10-3) go down. At the same

time, with increasing k, T u the second term goes up. Therefore, it can be expected that as
k increases, tcoll (the left hand side of Eq. 10-3) goes down up to a certain point and then

as the second term becomes dominant, it goes up again. Thus, we can expect to have a

17]

range of k for which rm” 3 I that defines the stability region. The lower threshold

referred to as ks,_ 10W below which the system is unstable and higher threshold is

k 5, high . Note that this analysis assumes that the nodes have infinite buffers and therefore

no packet losses are considered. The instability is defined only in terms of trip
collection-time when it exceeds 1. However, when nodes have limited buffers, instability

would also manifest in the form of unacceptable packet loss rates.

10.3 Impact on Stop Threshold
The MDH collects data from all Designated Gateways (DGs) once every collection cycle
(I). Hence, during each cycle, the data that has been generated during the most

recentz'units of time has to be uploaded. As mentioned earlier, Tu is the time required
to upload the data. If Tu S T p , the MDH can collect all the generated data without

stopping at any DG. In this case:

(r x ,1 x n)/Ceff s 3L/(2k +1)v or k .>_ (now/212221»)— 1/2

3LCefl

In other words, k stop = {-71:2—
1 v

] —% defines the threshold beyond which the MDH has

to incorporate stops. As pointed out in Chapter 9, this implies that if the value of k is
above this stop threshold, the mobile sink has to be capable of reducing its speed, stopping
or executing a holding pattern.
10.4 Impact of Capacity-awareness: Numerical Evaluation

The numerical evaluation of trip collection-time that was performed in Section 9.3 was
repeated using the expression for upload time obtained in Eq. 10-2. Trip collection-time

for a circular network terrain of radius 300m and sensor transmission range of 30m was

172

obtained by using Eq. 6—4. Thus, the minimum number of hops at which static sink

operation is possible is 300/30 = 10 hops (i.e. kcritical =10). The value of the collection

cycle, I was set to 6008 and the MDH speed v was set to 3m/s.
10.4.1 Trip Collection-time

Figure 10-2 shows the trip collection-time too” for different values of it and k
ranging from 1 to k ”in-ca, . For all it, as observed in Section 6.6.2.1, with increasing k,

the collection-time, tea” goes down fast until the stop threshold ks is reached. In

top
case of low it, ta,” goes down at a slower rate or stabilizes as seen in Figure 10-2:a. On
the other hand, when it is large, the trip-collection time goes up as k increases beyond the

stop threshold. To understand the behavior of trip collection-time, we plot Tm , T and

P

Tu in Figure 10-3. The behavior of move time, Tm and productive move time, T p is
same as observed in Figure 9-2. These quantities are not affected as they are not
sensitive to link capacity. On the other hand, data upload time, Tu depends on the

effective MDH-DG link capacity as mentioned in Section 10.2.]. In Figure 10-3, it can

be observed that Tu is relatively constant at low 1. As the data rate increases, the

upload time increases due to reduced effective capacity of the MDH-DO link. In Figure

10-3, the upload time, Tu increases with k due to the increasing interference

experienced by the MDH-DG links resulting in lower effective capacity, Cejf . Higher the

data rate, higher the rate at which Tu increases as observed in Figure 10-2 for 21 = 88

and 184 bps.

173

Note that values of k S 4 are unstable irrespective of the data rate as the move time,

Tm and therefore the resulting tea” is higher than 1'. For sufficiently high values of k and

x1 , the trip collection-time goes down and then begins to increase as the upload time
becomes dominant. In Figure lO-2zb, this phenomenon can be observed for A = 184 bps

where values of k 2 7 results in too” > r and therefore are unstable. Thus, there exists

a stability ceiling, ks,_ high beyond which the system is not stable. Thus values between
ks,_low and ks,_ high constitute the stable or feasible range for the hop-bound factor k.
When 1:88 bps, the feasible range is given by ks,_,0w S k S ken-ma, or4 S k S10. On
the other hand, for k=184 bps, the stable range is ks,_10w S k S ks,_ high or 4 S k S 7.

This implies that for sufficiently large value of A, static sink scenario (k 2 kcritical ) data

collection may not be stable. In such situations, using mobile sink for data collection and
choosing an appropriate k can make data collection possible.

As pointed out in Section 9.2.1, the stop threshold kmp indicates the point beyond

which MDH has to stop during its trajectory to complete data collection. Therefore, the rate
at which the trip collection-time decreases with k is lower after the stop threshold. This

implies that the energy cost associated with higher amount of multi-hop routing may not be

worthwhile. Hence, the desirable range of k is given by k s,_ 10,, S k S k 5,0], . Note that it

is possible for the stop threshold, k stop to be lower than k st_ 10W implying that all values

of k that result in stable operation require stops. In such cases, k has to be chosen from the

feasible region.

174

A

Unstable

 

 

Stable

 

2000 -
1800 -
1600 -
1400 -
1200 -
U)

‘2 1000 -
800 -
600 -
400 .
200 -

Unstable
A

Stable
A

 

 

A: 88 bps

   
 
 

tcoll

 

Egbilitv-mresholdl

 

[stop-th resh olcfl

 

  
  

 

4 5 7

Hop-bound k
(a)

1 2 3 6

tcoll

 

  
 
 

stability-threshold

 

 

 

 

 

[stability-ceiling]

 

 

 

stop-th resh old

    

 

r=.600 _,

Desirablel I Feasible
Mange/m k range

 

4 5 6 7 8 9 10
Hop-bound k

(b)

1 2 3

Figure 10-2: Impact of k on trip collection-time (Ceff < C ) for different data rates

175

2000 -
1800 -
1600 .
1400 .
@1200 - Tm T,I (k=184)
r-f 1000 - Tp ;/
"f 800 -

Unstable
k

 

E
'- 600 --~-_
400 . Tu (F83) ;

200 -
=42====§a=£§ , .=,;;:-:;fi-‘}__1:

   

Stable

0 1 2 3 4 5 6
Hop-bound k

 

FigurelO-3zlmpactofkon Tm, Tp and Tu

Figure 10-2:a depicts the scenario when the MDH-DG communication channel has the

same capacity as that of the DG—sensor channels. In this case, the effective capacity C efl

is lower than the total link capacity C. When a separate channel is used for MDH-DG

communication, then the transmissions in the vicinity of the MDH do not cause any

interference to the DG-MDH data upload rendering Cefl = c. In this case, the upload time,

too” goes down

Tu is not sensitive to k and only depends on A. . Therefore,

monotonically with increasing k. In other words, the behavior of tea], is very similar to

what is observed for A = 88 bps in Figure 10-2za. This separate channel scenario
provides the upper bound for data collection performance. Figure 10-2:b on the other
hand, assumes that the MDH-DG and the sensor-sensor communication occurs on a
single channel causing maximum interference for the upload activity. This case provides

the worst case Tu and trip collection-time performance and hence represents the lower

176

bound for ks! high .

1000 -
900 -
800 -
700 -
600 -
500 '

Unstable

tcoll (S)

400 -“

300 -
200 '
100 -

Stable

 

1000 -
900 -
800 -
700 -

A

Unstable

tooll (s

400 r

100 -

Stable

600 -,
500 'l,

300 -'
200 —,

 

I:

A=184 bps

r*’77*”””””“7‘
stabllltY'th "35h Old Feasible

krange

 
 
  
 

600s

i

Lstop-mfesfifi i

Desirable k ran ge

I l I l I

3 4
Hop-bound (k)

(a)

A=264 bps

 
 
    

    

 

I i I” 7' r 7 1'

3 4
Hop-bou nd (k)

(b)

Figure 10-4: Impacts of k on tea” for square network region

Non-circular Terrain: The numerical evaluation performed was repeated with square

177

terrain for the reasons outlined in Section 9.4.1. We evaluated the trip collection-time for
square network terrain with side = 500m, transmission range =50m and t = 7005. For this

network km-tica, = 7 . The trip collection-time results obtained for different data

generation rates for Stop-on-Demand data collection are shown in Figure 10—4. It can be
observed that the results are very similar to those shown in Figure 10-2 for a circular

region. Thus, the trip collection-time trends and the observed thresholds hold in general.

10.4.2 Stop and Stability Thresholds
In this section, we focus on the impact of various network parameters such as data rate,
node density, link capacity and MDH speed on the thresholds associated with k and

therefore the feasible range of k.

 

10 -
kst_high

MK

Degree of Multi-hop Routing (k)
N

 

 

 

5 _

4 ..

3 q kst_low

2 I I l _I I l I I

20 70 120 170 220 270 320 370 420
Mbps)

Figure 10-5 : Impact of data rate on the stability range and st0p threshold
Figure 10-52a shows the impact of data rate on kstJow, ks,_h,-gh and kstop. As the

data rate increases, the value of k S, 10,, stays constant because it depends only the move

178

time for the trajectory which is only dependent on k. However, ks, _ high goes down as

the amount of data that needs to be collected goes up increasing the stop durations
ultimately causing trip collection-time to exceed r. Consequently, the range of k that is
stable goes down until it shrinks to zero implying that the system is not stable for any
amount of multi-hOp routing. For instance, in Figure 10-5, for 1:384 bps, the stability

range is zero. On the other hand, k stop goes down with increasing data rate as the upload

time resulting in the need for stops at lower and lower values of k. Note that kstop is
always lower than the stability ceiling i.e. kstop S ks, high . Since inclusion of the stops

due to reducing capacity is what drives the ta,” up, stop threshold has to lie before

 

kst_high-
10
S 9 -
°’ kst_high
F 8 -
a
3 7
m -
8 6 -
.1; kst_lo
E 5 —
3
2 4 -
“5
a) 3 ‘
e kstop
g 2 4 l
D 1

 

 

 

0 0.5 1 1.5 2 2.5 3 3.5 4
Nodes/100 m2

Figure 10-6: Impact of node density on the stability range and stop threshold

179

A
_3

 

 

 

S?

v 10 . .
2’ kst_high

:3: 9 ~

8

a: 8 .

o.

O 7 ~ I
i 6

g 5 q kstop

“5

g 4 .4 b
D) 3 1

8 2 kst_low

 

1000 2500 4000 5500 7000 8500
Capacity of MDH-DG Iin ks (kbps)

Figure 10-7: Impact of link capacity on the stability range and stop threshold

Figure 10-6 shoWs the impact of increasing node density on the thresholds. Node
density affects the thresholds in the same manner as it because in both cases the amount of
data that has to be collected from each DG goes up. In other words, the stability range
shrinks with increasing density and the stop threshold goes down. The stability threshold,
kst_low increases due to the increasing trip collection-time resulting from inclusion of
stop durations. At the same time, the stability ceiling ks,_ high comes down due to the
increasing trip collection-time resulting from higher upload times due to reduced effective
link capacity.

For a given node density and it, Figure 10-7 shows the impact of the capacity of the links

on the stability range. The impact is exactly the opposite of increasing the data rate or

density. This is because as the link capacity increases, the MDH stop durations shrink or

are not required. This leads to reduced ta,” causing kstop and ks, high to move

upwards enlarging the stability range. The impact of MDH speed is shown in Figure 10-8.
1 80

As the speed increases, the time required to complete the trajectory or the move time, T m

goes down. As a result, the time during which data can be collected, T p also goes down

resulting in stop durations being required at lower values of k. Therefore, kstop goes

down. The decreasing move time also leads to significantly lower trip collection-time at

lower values of k causing the stability threshold, k s,_ [ow to go down.

 

 

Degree of Multi-hop Routing (k)

 

 

 

 

MDH speed (m/s)

Figure 10-8: Impact of MDH speed on the stability range and stop threshold

10.4.3 Discussion

Based on the analysis thus far, the following inferences can be drawn.

1. For given network parameters, there is a lower-bound of k (kst‘ low) for stable

data collection which implies that single hop collection may not always be
feasible. If MDH-DG link and sensor-sensor links use the same channel for
communication, then for sufficiently high values of l and k, there exists a

stability ceiling, k s, _ high beyond which the system becomes unstable.

181

2. For sensor network scenarios with sufficiently high data generation rate static sink
data collection can be unstable. In such cases, employing a MDH for data

collection offers a viable solution.

3. In applications where it is required to use value of k < k S, 10,, or k > ks, high

or A > C/ n , multiple sinks should be deployed for data collection.

4. If 2. is low enough to allow kst_low < kstop, then it is desirable to pick a k in

between them. The reduction in t c0” beyond kstop is small since stops have to

be incorporated, but higher routing congestion is incurred due to increased
multi-hop routing.
10.5 Performance Evaluation via Simulation Experiments
The validity of the performance trends predicted by the model was verified using an
n52 simulation model with IEEE 802.11 as the MAC layer protocol. If data collection is
incomplete within the contact duration, the MDH stops at the Navigation Agent to upload
all the buffered data from the DGs before moving to the next DG Note that the
simulation results are not expected to be numerically comparable with the results in
Section 10.4 for two main reasons. First, the model assumes a perfect collision-free MAC,
whereas the simulation uses 802.11 with all its associated overheads, including collisions.
Second, the model assumes infinite buffer availability whereas in simulation buffers are
limited.
We conducted our baseline experiments on the 144-node network with nodes arranged
in the form of a regular square lattice. As mentioned before, with this network size and
the chosen wireless transmission range of 30m, the hop-bound factor k can be varied

from 1 through 5 for the mobile sink scenario. The case for k=6 represents the static

182

sink scenario indicating that ken-“ca, =

6 . Unless otherwise stated, the speed of the
MDH was set to 3m/s. The duration of the collection cycle (I) was set to 5003. For all
experiments, we measured the trip collection-time.
10.5.1 Impact of Data Rate on Performance

The trip collection-time obtained by varying the data generation rate for different values

of k is shown in Figure 10-9. The results indicate that for lower data rates (k=128 bps) as

k increases, the trip collection time, ta,” goes down steadily due to reduction in

trajectory length as seen in Figure 10-10. However, for higher values of data rate

( 2.2 512bps) although ta,” tends to decrease as k increases initially, the trip
collection-time tends to go back up at higher values of k. The initial decrease in ta,”

that is observed with increasing k is driven by shrinking trajectory length. However, at
the same time, there is an opposing effect that tends to push the trip collection-time
upwards. This effect is driven by two phenomenon that occur as k increases namely 1) the
amount of data accumulated at each DG increases due to higher multi-hop routing 2)
the amount of interference in the network also increases resulting in reduced effective
capacity of the DG MDH link. These effects lead to higher upload times forcing the
MDH to incorporate stops into the trajectory. Inclusion of stops into the trajectory

darnpens the reduction of ta,” as k increases and at some point overtakes the

tea” reduction due to shorter trajectory.

183

90° ‘ +128 bps +512 bps
800 - +1024 bps +1280 bps
+ 1462.8 bps

Trip Collection-time (s)

 

 

100 . . . 1
1 2 3 4 5
Hop-bound (k)

 

Figure 10-9: Impact of k on trip collection-time

1600 -
1400 -
1200 -
1000 -
800 -
600 ~
400 -
200 ‘

Trajectory Length (m)

 

 

Hop-bou nd (k)
Figure 10-10: Impact of k on trajectory length

In Figure 10-9, it can also be observed that as the data rate increases the rate at which
the trip collection-time reduces is lower. Also, the value of k at which the trip
collection-time goes up is lower. For example, for 1=1024 bps, the trip collection-time

goes up at k=5 while for k=1462.8 bps, it happens at k=3. Thus, the feasible range shrinks

184

as the data rate goes up until no k is feasible. These trends match the predictions of the

analytical model as shown in Figure 10-2.

100 +k=1 +k=2 +k=3
90 . + k=4 —)t— k=5

80*
70« \
60— .
50-
40~
30~
20— \fi;
10- 4

O I 1 I I I I I— I

100 700 1300 1900 2500 3100 3700 4300 4900

ll
11
I'.’

/ 1

Packet Delivery Ratio (%)

 

 

 

 

Data Rate, A (bps)

Figure 10-1 1: Packet delivery performance for varying data rates

120000 '

 

 

     

 

 

 

 

 

 

 

 

 

 

lHop-5
100000 - Wop-4
w IHop-3
3 80000 . “HOP-2
'o DHop—1(DG)
3%. 60000 - __
2 \
20000 ~ \
0 r m r r r r
1 2 3 4 5

Hop-bound (k)
Figure 10-12: Drops experienced by nodes at different hop distance from the Navigation
Agents

To understand the capacity issue further, we collected the packet delivery and drop

185

statistics during the experiments. The packet delivery and MAC layer drop values
obtained are shown in Figure 10-11 and Figure 10-12. As the data rate increases, the
packet delivery is stable initially and then tends to drop. Higher the value of k, sooner the
drops and higher the rate of packet drop. Since the MDH stops to collect data from the
DOS, the low packet delivery ratio seen in Figure 10-11 at higher data rates is not
intuitive. Figure 10-12 shows the drops seen at the 802.11 MAC layer at different
distances from the Navigation Agents for A=2048 bps. It can be observed that the number
of drops is negligible for k=1. As k increases, nodes at higher and higher hop distances
from the Navigation Agents experience packet loss. This is due to increased congestion at
the MAC layer indicating that link capacity is insufficient to handle all the data generated

in the network for the given values of A and k.

10.5.2 Impact of Network Parameters on the Feasibility Range and Stop Threshold

 

 

 

Hop-bound Thresholds (k)
(.0

 

 

0 300 600 900 1200 1500 1800
Data rate, A (bps)

Figure 10-13: Impact of data rate on the feasibility range

Now, we explore the impact of data generation rate on the feasibility range for the

186

hop-bound factor through simulation. As observed in Section 10.5.2, while the stability

threshold kS,_ [ow goes up, k 5, high is expected to go down resulting in progressively

reducing the feasibility range. The results obtained from the simulation validate this

trend as shown in Figure 10-13. The k st_high drops with increasing data rate as the

upload time increases causing tea” to increase at lower values of k as seen in Figure

10-9. ks, 10w goes up with increasing data rate also due to the same effect namely

increase in upload time causing the lowest value that results in tea” < A to increase.

The feasible range shrinks as a result.

We conducted some experiments to study the impact of node density on the feasibility
range of k. For the experiments we used the number of nodes in 240x240 meter area to be
144, 265 and 386. Figure 10—14 shows the feasibility range of k for the different densities
(expressed in number of nodes per lOOOmz) for A=1024 and 512 bps. In both cases, it is
clear that the feasibility range shrinks with increasing density. With higher data rates, the
rate at which the range shrinks is higher. For example, when A=1024 bps, k=2 is the only
feasible value for 265 node network. For A=512 bps on the other hand, the feasible range
is 1 S k S 5 . For 386 node network, when A=1024 bps there is no value of k that is

feasible. In other words, even single hop data collection (k =1) is unable to meet the

too” S r constraint. On the other hand, for the same network when A=512 bps, the

feasibility range isZ S k S 4. These results from simulation validate the impact of

density forecast by the model in Section 10.4.2.

187

6 ‘ A=1024bps

k st__ high

    

 

Hop-bound Thresholds (k)
OJ

 

 

 

 

 

 

1 q kst_low
O I I I I I
2 3 4 5 6 7
Nodes/1000m2
(a)

6 ‘ x: 512bps

5 ~ I
.8 4 - kst_high
To
a 3-
E
.2 2 “ kst [OW ./
3
O
a 1 - F \
O
I

0 I j I I I

2 3 5 6 7
l\lodes/1000m2
(b)

Figure 10-14: lrnpact of density on the feasibility range for different data rates

Finally, we conducted experiments with varying MDH speeds from lm/s to 20m/s.
The data rate in this experiment was set to 1024 bps. The results obtained for the
feasibility range shown in Figure 10-15. With increasing speed, the feasibility range

expands as expected from the analytical model (see Figure 10-8). As the speed increases,

188

the trip collection-time decreases allowing lower values of k to result in trip

collection-time to be less than t. This leads to the drop in ks,_10w . In this experiment, the

k S, _ high stays at the highest possible value (k=5) for all values of the speed.

 

 

Hop-bound Thresholds (k)
OJ

 

 

2 ~ kst_low
1 - \. \l e e e
O 1 I I T l
0 5 10 15 20 25
MDH Speed (m/s)

Figure 10-15: Impact of sink speed on the feasibility range

10.6 Summary and Conclusions
In this chapter, we have extended the generalized hop-bound model to accommodate
the MDH-DG link capacity constraints. Upload time and trip collection-time equations

were modified appropriately. A new stability threshold referred to as k S,_ high was

identified which provides the upper bound for the feasible range of k. The impact of
capacity constraints on trip collection-time and the thresholds were numerically evaluated.

The trends obtained from the analysis were validated using simulation experiments.

189

 

 

 

 

 

A
8
(a) LowA *"
A
(b) HighA
Feasible
Range \
k
A
_ kstop
I";
(c) Very HighA 7‘ .. ________________ .
k

 

Figure 10-16: Possible feasible range scenarios

The analysis and experimental results presented in this chapter underscore the
importance of taking the data generation rate (A) into account for trajectory planning.

When A is small relative to the MDH-DG link capacity, sink trajectory can be based on

hop-bound factor within the feasible range spanning kmbih-O, (or k S,_ low) and ken-“cal.

However, for larger A, beyond the stop threshold, the trip collection-time (‘coll ) is pushed

190

by increasing upload time. If the data rate is large enough, ta,” can exceed the
collection cycle duration for somek <km-tica, referred to as k 5, high- Thus, the
feasible range is constrained within k 5, high implying that static sink data collection is

not always feasible. Figure 10-16 shows the possibilities for the feasible range obtained

for varying data rates. As long as ks, low is non-zero (or tea], crosses r), irrespective

of kstop, the feasible range is ks,_10w to k s,_h,-gh. However, in very high data rate

case where the trip collection-time is driven up by increasing upload time before the trip
collection-time reaches 1, there is no value of k for stable operation. Thus, the data
generation rate of the nodes is a key parameter in determining the feasible region of the

hop-bound factor that can be used for trajectory determination.

191

Chapter 11. Rate Aware Sink Trajectory Design for Heterogeneous Networks

11.1 Motivation

In Chapters 9 and 10, model and simulation results have established that the data
generation rate of the nodes have a significant impact on the hop-bound thresholds and
therefore, on the feasible range of k for stable operation. Thus far, we have assumed
uniform data generation rate for all nodes. However, it is not difficult to foresee
applications where the data generation rates are different in different regions of the
network. For instance, in multi-mission sensor networks, data generation rates are
dictated by the requirements of multiple applications or missions, and hence may not be
uniform across different areas. In addition, concurrent applications [65, 66], differential
aggregation in various parts of the network [151], and variations in sensing granularity
depending on the quality of data required [152] could result in spatially varying data rates
across the network. In such networks, a single value of k may not be able to balance the
trip collection-time and energy consumption or lifetime, as different regions may have a
different feasible ranges of k. Consequently, strategies for finding a sink trajectory with
varying effective k based on heterogeneous ,1 need to be investigated.

Consider the example network shown in Figure 11-1. It has six regions, each with a
different data rate. Let A” denote the data rate of nodes in region-x. In the example

network, assume It” > 1’3 . If a single value of k is chosen for the entire network, then

the MDH trajectory is evenly distributed among the regions. However, it seems intuitive

that in areas where there is more data due to higher data rate such as Ar] , the sink should

visit more DGs to collect data to distribute the energy load. This implies that a lower

192

amount of multi-hop routing (k) should be used for regions with higher data rates and

vice versa.

 

Trajectory in

, 0
region 1 :

Trajectory in

7 region 3

 

 

 

 

Figure 1 1-1: Network with six regions

11.2 Related Work

In the existing literature as discussed in Section 2.4.2, [7] considered data collection
using a mobile element from a set of Designated Gateways. However, the trajectory of
the MDH was fixed and thus the nodes that came in direct contact with the MDH
performed the role of DGs. Based on a specified trip collection-time constraint, the
authors proposed to adapt the speed of the MDH based on the amount of data currently
buffered at the DGs. The basic idea is to allow a subset of nodes with lot of buffered data
to have longer contact time with the MDH during each trip. The goal is to maximize the
quantity of data collected from each node. In this case, since the extent of routing is
always constant, the amount of buffer required was higher and inversely proportional to
the speed of the mobile element. In [7], the only factor that affected the packet delivery
performance was MDH speed since the MDH path was fixed. The goal of this chapter is

to study the inter-play between the trajectory based on the hop bound factor k (i.e. the

193

extent of multi-hop routing), the heterogeneous data rate and the packet delivery
performance.

The authors of [97, 98] consider the problem of scheduling mobile elements for
single hop data collection to meet the data delivery deadlines and buffer constraints. The
MDH has to visit each node with the frequency required to ensure that its buffer does not
overflow. Since the problem was limited to single hop data collection, energy spent per
packet or trip collection-time was not considered for the purpose of optimization. In this
thesis, the hop bound factor k optimized for different regions with respect to overall trip
collection-time, energy consumption and delivery performance will be considered. To our
knowledge, optimization of the trajectory for heterogeneous network and traffic model in
the context of multi-hop routing as proposed in this thesis has not been considered before.
11.3 Analysis of Region-specific Multi-hop Routing

In Section 9.2, the stop and the stability thresholds for k were discussed. These
thresholds determine the feasible operating region for k and they depend on the data
generation rate. Note that the trajectory length and AEPP are independent of A. , as

derived in Sections 6.6.] and 6.6.3 respectively. The upload time Tu and hence the trip

collection-time [€011 depend on it. For a given set of network parameters, as the data

generation rate increases, the load on the DG-rooted routing trees increases. This also
implies that the MDH would need to collect higher amount of data in the vicinity of an
NA. As before, this results in the thresholds being moved to the left. In addition, the
increased load on the different annular regions of the tree (see Figure 6—8) leads to higher
variation in energy drainage rates around the DG resulting in increased SDRE. Increased

SDRE for a given AEPP (only depends on k) would be expected to affect lifetime

194

adversely. This implies that in heterogeneous networks, a single value of k for the entire
network would not suffice. In order to achieve global objectives such as stable data
collection, better trip collection-time and distributed energy drainage, different amount of

multi-hop routing (k) would be required in different regions of the network.

 

     
 

Require fewer
drops

 

 

Figure 11-2: Data-rate aware regional k selection procedure

In the following sub-sections, we present a specific example to outline the problem
with using single value of k for the entire network regardless of data rate variations.
The process of choosing different values of k for regions and the potential benefits are

discussed. We consider an example where a network consists of two regions with data

rates [1’1 and 1'2 respectively such that A” >/1r2. Figure 11-2 summarizes the

process of choosing the extent of multi-hop routing for the regions using the example
network. The k values chosen for region-land region-2 are denoted by krl and krz.
We conducted experiments with 1’1 =1024 and [1'2 = 205 bps with the duration of

195

the collection cycle 1: set to 4003. Applying the insight that higher data generation rate

requires a lower k, the k pairs were chosen such thatkr1 S kr2 . Figure ll-3 thru Figure

11-5 that shows the performance metrics for different regional k pairs.

To begin with a single value of k for the network, say kp is chosen based on the

primary requirement of the application (energy consumption or trip collection-time).
The combined trip collection-time of the MDH is computed as follows. First, the set of
Navigation Agents is identified in each region for its respective k value. Second, the tour
distance is computed by solving the TSP problem on the set of Navigation Agents
obtained by aggregating the NAs across all regions. Lastly, the tour distance is divided by
the speed of the MDH to obtain the trip collection-time.

There are two possibilities based on whether tea” satisfies the collection cycle

constraint or not. If it does not, then the value of k in the lower data rate region is
incremented and the process of computing the trip collection time and checking the
stability condition is repeated (Section 11.3.1).

500 -
450 ~ —
400 -_ ._ . __________________________
350 -
300 “
250 r
200 -
150 -
100 -
50 -

O ' 7 ' ' t a
1,1 2,2 3,3 1,2 1,3 1,4 2,3 2,4 3,4

k", k'2

Figure 11-3: Trip collection-time for different combinations of regional k values

r-unstable

I l I l_:::;

Trip Collection-time (s)

[I
F
I.

. Z'Pi': .
Il-l-I-I-I-l-I-I-I-

 

 

 

 

:I'I

 

 

I96

If the trip collection-time is within the specified collection cycle duration, then the
application requirement determines the next action. If the application requires better
energy load distribution or lower trip collection-time (Section 11.3.2), then the k value for
the lower data rate region, namely region—2 is incremented and the stability check is
repeated. On the other hand, if the application requires lower drops or data accumulation
(Section 11.3.3), then the k value in the lower data rate region namely region-1 is
decremented.

11.3.1 Unstable Operation “call > r)

In heterogeneous networks, if a single value of k is chosen for the whole network, then

it is required that the k be chosen to meet the primary needs of the application.

180-
160-
140‘
120-
100~
80-
60~
40-
20-
0-

     
     
   

    

AEPP (mJ)

   

 
     
       
 
      
   
   
     
       
  

    

 

1A 22 33 12 13 1A 23 2A 3A

Figure 11-4: Energy metrics for different combinations of regional k values
If an application requires that the network be operational for a long time and desires to
choose a k value for both regions, then krl = kr2 =1 is the obvious choice. However,

that results in a trajectory that is unstable as seen in Figure 113 because too” > 2'.

Alternatively, choosing kr1 = kr2 = 2 results in a stable trajectory, but it is not

197

desirable due to poor energy and drop performance as seen in Figure 11-4 and Figure

11-5. If k were to be chosen on a regional basis, then a higher value of k, say 2 can be

assigned to region-2 with lower data. The resulting combination k r1 =1 and kr2 = 2

leads to a trajectory that can satisfy the collection cycle constraint and yet achieve better

energy and drop performance compared to kr1 = kr2 = 2.

11.3.2 Stable Operation (tcoll > r ) with High Energy Expenditure

1.8 -
1.6 -
1.4 -
1.2 ~
1 -
0.8 '
0.6 ‘
0.4 ‘
0.2 ~
0 1 1 — 1 - 1 1
1,1 2,2 3,3 1,2 1,3 1,4 2,3 2,4 3,4
k”.W

% Drops

 

 

(a)

% Residual Data

 

1,1 2,2 3,3 1,2 1,3 1,4 2,3 2,4 3,4

(13}
k”, kr2

Figure 11-5: Drops and data accumulation for different combinations of regional k values

198

An application requiring to operate at the lowest k that results in a stable trajectory will

need to choose krl =kr2 =2. Although the resulting trajectory is stable, it suffers
from high energy expenditure and poor drop performance as seen in Figure ll-4 and
Figure 11-5. If k were to be chosen on a regional basis, then a lower value of k, say 1

can be assigned to region-l with higher data rate to improve the energy and drop metrics.

The resulting combination krl =1 and kr2 =2 leads to a trajectory that can satisfy

the collection cycle constraint and yet achieve better energy and drop performance

compared to kr1 = kr2 = 2.

11.3.3 Stable Operation (teal, > 2') with High Packet Accumulation and Drops

500 -
450 ~ —1 Low exposuretime
400 -. . _________
350 -
300 -
250 -
200 -
150 -
100 ~
50 -
o .

Trip Collection-time (s)

 

 

 

   

 

1,1 2,2 3,3 1,2 1,3 1,4 2,3 2,4 3,4
k", k’2

Figure 11-6: Trip collection-time for different combinations of regional k values

Now, consider an application whose prime requirement is low exposure time. In such a

case, krl =kr2 = 3 which results in the lowest trip collection-time (see Figure ll-6)
among the network-wide k assignments, is the logical choice. However, the resulting
trajectory suffers from higher consumption energy consumption, increased drops and data

199

accumulation at the DOS as seen in Figure 11—4 and Figure 11-5. In this context,
choosing a region specific k obtained by reducing the value of k in region—l which has

higher data rate can address the energy and drop issues. For example, the combinations
kr1 = 2 and kr2 = 3 or kr1 = 2 and kr2 =4 offer exposure time comparable to

krl = kr2 = 3 (see Figure 11-6) and at the same time offer better performance in terms
of energy per packet, variation in energy usage, packet drops and data accumulation

(see Figure 11-4 and Figure 11-5).

11.4 Problem Formulation

Consider a network with m regions such that a region i has a data rate of Ari with an
MDH that collects data in Collect-on-Move mode. The capacity of the MDH-DG link
(C), the speed of the MDH (v) and the duration of the collection cycle (I) are given. The
problem is to find a set of Navigation Agents such that the resulting MDH trajectory

satisfies the following:
1) The network operates within the stop-threshold (kstop)
2) The trip collection-time (tool!) is minimized.
Note that constraint 1 inherently ensures that the extent of multi-hop routing in areas
with higher data rates is lower and vice versa. Once the Navigation Agents are
identified, data collection proceeds as described in Chapter 8. The trajectory resulting

from the selected NAs has to be verified for compliance with the collection cycle (I)

constraint.

11.5 ,1 -Aware Trajectory Determination (LATD)

In this section, we develop a it -aware Trajectory Determination or LATD algorithm

200

based on the NADC framework presented in Section 7.2. The design goal of LATD is
to select the Navigation Agents such that data collection can be completed without
requiring the MDH to stop. This implies that the NA selection process has to ensure
that the data buffered at the DOS can be collected within the contact duration between the
MDH and the DOS around a Navigation Agent. The NA determination algorithm
(phase-l of NADC algorithm) described in Section 7.2.1 was data rate and link capacity
independent in its operation. In this section, extensions to the NA determination
algorithm required to achieve data rate awareness are presented. Rest of the NADC
components can be used without any modifications.

11.5.1 Protocol Details

The Navigation Agent selection algorithm runs in multiple phases:

1 1.5.1.1 Data Forwarding Tree

In order to choose NAs in a data rate aware manner, LATD requires that every node
keep track of the amount of data that is routed through it to the MDH. For this purpose, a
tree is created rooted at a node referred to as the Lambda-Root. The tree construction is
accomplished using the neighbor discovery Hello messages. In the Hello message, every
node sends its data accumulation information per unit time, El which is computed as the
sum of:

1) Data that is forwarded by its children in the tree per unit time

2) Data generated by itself per unit time (xi)

201

 

Region-1 Region-2
(20,0) :

   
   
 

  
  
   
   

  

. (20.20) . (10,0) (10,0)

(10.40)

(10.60) . (10,10)

  
    
      
    

(10.80) (10.90)

 

(20,40)

(20,40)?

7. Lambda-Root

   

 

(10,60) (10,230)

(20.20)

 

 

 

Figure 11-7: Example to illustrate the tree rooted at Lambda-Root

At the end of this phaSe, each node knows the amount of data that it has to upload to the
MDH. This quantity is referred to as local data accumulation, El. Figure 11-7 shows an
example network which consists of nodes that generate data at two different rates namely
solid filled nodes generate data at 20 bps and clear filled nodes at 10 bps. The tuple next
to each node is of the form (data generated per unit time by the node, data forwarded by

the children per unit time) and the two values combined provide El.

1 1.5.1.2 Navigation Agent Selection

As mentioned in Section 7.2.1, the data rate unaware NA determination phase begins by
establishing a tree at an arbitrarily chosen root node referred to as NA-Root in this
section. In LATD, the NA-Root is chosen such that it is as far from the Lambda-Root as
possible in terms of hop count as shown in Figure 11-9. The figure also shows the tree
created for the example network. This ensures that the NA determination protocol
messages flow towards the Lambda-Root and allows the protocol to estimate the local

202

data accumulation at nodes to be as accurate as possible.

 

Algorithm for A-Aware Navigation Agent Identification:

Process Declare-NA message from a neighbor:

Mark neighbor as a Navigation Agent in neighbor table
Set neighbor data-rate to zero

Broadcast Accept-NA message

Process Accept-NA message from a neighbor:
Call ComputeCumDataRate
// check if the amount of data accumulated is higher than upload capacity
if( (cum-data-rate* r*y) > (C * transmission-range / MDH-speed)) {
Mark myself as a Navigation Agent
Set [Etc 0
Broadcast Declare-NA message to all neighbors
} else {

re-broadcast Accept-NA message

}

ComputeCumDataRate 0:
Set cum-data-rate to zero
// compute cum-data-rate as sum of data rate from all children
For (each neighbor in Neighbor-List) {
if (I am neighbor’s parent) {
Add neighbor’s Elto cum-data-rate
}
}

return (cum-data-rate)

 

 

 

Figure 11-8: Pseudo code for data rate aware NA selection

The root node initiates the NA discovery process by sending out an Accept-NA
message. Every node in the network has an estimate of the amount of data that it can

upload to the MDH when it comes in contact which is referred to U max . U max is

computed by multiplying the link capacity of the MDH-DG link with the amount of

203

contact duration with the node. The contact distance can range from negligible to 2*r

where r is the transmission range. Thus, on average a node can expect to have a contact

r
time of —. So, Um,x =(C*:). In the example network shown in Figure 11-9, the
v v

values of transmission range r, MDH-DG link capacity C and MDH speed v are assumed

to be 1m, 40 kbps and lm/s respectively resulting in:

1m_

 

 

U max = 40kaS * — 40kbits ,
1m/ 5
NA-Root Region-1 Region-2
it r
9 . .
1' .
o O '

 

 

 

 

 

Figure 11-9: Tree rooted at NA-Root

Also, the local data accumulation per second, E] which was computed during the data
forwarding tree creation process outlined in Section 11.5.1.1 is maintained at every node.
The neighbors that receive the Accept-NA message compute the amount of data
accumulated during a collection cycle as 1 *¢* 7. 7 is the scaling factor that is
applied to the accumulated data to take into account the message overheads and the

interference from other DGs in the neighborhood uploading their data at the same time to

204

the MDH. 7 is always greater than 1.

 

Region-1 Region-2

. (10,0) (10.0)

  
  

 

. (10,10)

(10,40)

 

  

 

3

(20,20) (10.60) (10,30)

 

 

 

Figure 11-10: NA selection based on data accumulation and the resulting trajectory

If r * ¢ * y 2 U max , the node declares itself as a Navigation Agent by broadcasting a

Declare-NA message. When nodes receive the Declare-NA message, the nodes that are
have lower hop distance to the NA-Root ignore it. Nodes that have a higher distance to
the NA-Root perform the following actions:
1) Mark the originator of the Declare-NA message as an NA node and set its [1 value
to zero.
2) It marks itself as a non-NA node broadcasts an Accept-NA message.

On the other hand, if 1 *¢* 7 < U max , the recipient node marks itself non-NA and

broadcasts an Accept-NA message . This process continues until all nodes are marked as
NA or non-NA nodes. Figure 11-8 presents the pseudo code for the LATD algorithm.

In the example shown in Figure 11-10, the total amount of data accumulated at the

205

nodes is calculated as 2' * (15 * y as mentioned above. The value of 7 was chosen to be

8. NA nodes are selected wherever the total amount of data accumulated exceeds

U max (calculated as 40kbits earlier). Based on that logic, nodes shown with dark

circles in Figure 11-10 are chosen as the Navigation Agents and the resulting trajectory is
indicated.
1 1.5.2 Protocol Behavior Analysis

The goal of LATD protocol is to identify NAs such that the resulting trajectory length is
minimal and allow the MDH to collect all the data buffered at the DOS during a round
trip. Therefore, the amount of residual data remaining at the DOS after contact with the
MDH is negligible. LATD achieves this by identifying NAs along the tree rooted at
Lambda-Root only where enough data has been accumulated to utilize the estimated
contact duration efficiently. Therefore, the trees rooted at the DOS should tend to be
longer in the lower data rate regions than in the higher data rate regions. Therefore, the

hop distances of the nodes in heterogeneous regions would be expected to exhibit

different profiles.
70 ‘
rl_ r2=

g 60- V A —1024,A 204.8pps
g 50- g
= /
q, 40- ¢
0’ /
g 30‘ /
8 /
a 201 /
a A

10~ /

,_ a

 

   

_x
N
00

4 5 6 7 8
Hop Distance

Figure 11-11: Distance of nodes from the MDH

206

We conducted data collection experiments with LATD as the trajectory determination
mechanism on a two region network with nodal data rates of 1024 and 204.8 bps in the
two regions respectively. The hop distance of every node to the MDH was collected.
Figure 11-11 and Figure 11-12 show the hop distance information for the network as a
whole and separately for the two regions. Results show that the region with the higher
nodal data rate has lower hop counts. For example, in region-l with 1024 bps, the hop

distance of the nodes range from 1 to 4. In contrast, in region-2, the hop distance ranges

from 1 to 8.
80 n
,, 70 . 231:1024, 71r2=2048pps
Q)
'0
g 60 J Bregion-1
g, 50 ‘ . Iregion-2
S
C
Q)
2
m
o.

 

 

1 2 3 4 5 6 7 8
Hop Distance

Figure 11-12: Regional breakdown of hop distances to the MDH

11.6 Simulation Performance Evaluation

LATD (A -Aware Trajectory Determination) protocol as described in Section 11.5 was
implemented in n32 and experiments were conducted to evaluate its performance. The
proposed LATD protocol was used in lieu of the Navigation Agent determination
algorithm (described in Section 7.2) in NADC framework. The results obtained from the

simulation experiments are presented in this section.

207

In Section 11.3, a detailed case was presented to choosing a k in different regions of the
network based on data rates. The goal was to achieve data collection stability (from r
perspective) or better drop and residual data accumulation performance. In this

sub-section, the trajectory obtained with the LATD algorithm is compared with the best
trajectories identified using different regional k values namely (k'l =1,k’2 = 2) for
achieving stability, and (ltr’1=2,k’2 =3) or (k’1 = 2, k’2 =4) for drop and data
accumulation performance.

11.6.1 Performance Comparison of LATD with Regional k Selection

500 'I
450 r
400 ~- - - -
350 ~
300 —
250 ~
200 —
150 -
100 -
50 -
0 1 ' ‘ 1
LATD 1,1

I
I
I
I
1
I
l
I
I
I

Trip Collection-time (s)

 

 

 

 

3,3 1,2 1,3 1,4 2,3 2,4 3,4
k’1 k’2

Figure 11-13: Comparison of LATD trip collection-time with regionally selected k

trajectories

208

1.8-
1.6 -
1.4 -
1.2 —

0.8 -
0.6 -
0.4 -
0.2 -

% Drops

 

LATD1,1 2,2 3,3 1,2 1,3 1,4 2,3 2,4 3,4

k”, k’2

% Residual Data

 

LATD1,1 2,2 3,3 1,2 1,3 1,4 2,3 2,4 3,4
k’7,k’2

Figure 11-14: Comparison of LATD packet drop and accumulation characteristics with
regionally selected k trajectories

The trajectory found by LATD results in trip collection-time that satisfies the collection
cycle (I) constraint similar to the regionally selected k values as seen in Figure 11-13.
Also, Figure 11-14 shows that LATD trajectory has no residual data accumulation and
drops indicating that it is operating within the stop-threshold. Close observation of

results presented in Figure ll-l3 thru Figure 11-15 show that the performance of LATD
matches that of (k’1 = 2 , k’2 = 3). In comparison to (k’1 =1 , k’2 = 2) and
(k’1 =2 ,k’2 =3 ), the trajectory resulting from (k’1 = 2 ,k’2 = 3) represents the

209

solution that minimizes the trip collection-time as much as possible while maintaining
good drop and data accumulation performance (indicative of operation with'm the
stop-threshold). The fact that the performance of LATD matches (k'1 = 2 ,k’2 = 3)
proves that the design goals of LATD as outlined by the problem formulation in section

11.4 are being achieved by the distributed LATD algorithm.

 
  
   
  
   

       
       

    
  

     
 

 

180-

um-

140 _ AEPP(mJ)

120—

100- ,
80- :53; it
60- 5:5 if: g
40 - l-: '2'!

0 _ 1:: 31:: $1

UUD 1A 22 a

w

12 13 1A 23 2A 3A
R", kr2
Figure 11-15: Comparison of LATD energy metrics with regionally selected k trajectories

11.6.2 Performance Comparison of LATD with Unconstrained NA Selection

In this sub-section, the following scenarios in networks with different data rates are

compared:

a) Unconstrained NA selection scenario where a single value of k chosen for the
whole network (Section 7.2).

b) A-aware Trajectory Determination algorithm (Section 11.5)

11.6.2.1 Two-region Network Scenarios

Simulation experiments were conducted on a network with two regions each consisting

of 64 nodes. An MDH moves continuously at a constant speed of 3m/s and collects data.

210

The collection cycle duration (1:) was set to 400s. Also, the scaling factor, 7 discussed in
Section 11.5.1.2 was set to 8. For the single k scenario, the value of k assigned for the two

regions was chosen such that the following conditions are met:
1) The resulting trajectory is stable i.e. 16.0115 T .

2) The drop performance for data collection along the resulting trajectory is

acceptable. The threshold for acceptability was defined to be 2% in this case.

The nodal data rates for the two regions, ,1” and 1’ 2 were set to three values namely
(512,102), (1024, 205) and (1280, 512) bps respectively. These data rate combinations

are presented as they essentially summarize the working of the LATD algorithm.

450 ‘ a kr1=k'2=2 I k'1=k'2=2 ca LATD
400 .
350 -
300
250
200
150
100
50

0

  

Trip collection-time (s)

 

512,102 1024,205 1280,512

)Vrl er
Figure 11-16: Trip collection-time for different regional data rates

Figure 11-16 shows the trip collection-time obtained for the experiments. In the

(512,102) case, the k'1 = k’2 = 2 solution results in a trajectory that has a relatively

high tea” value, but no residual data accumulation and negligible drops (0.2%). On the
other hand, k’1 = k’2 = 3 results in a shorter trajectory, but with 4% residual data

211

accumulation at the DGs and relatively high dr0ps (0.6%). In other words,

k’1 = k’2 = 3 is operating beyond the stop-threshold.

 

 

7 ‘ :1 kr1=kr2=2 I k"=k'2=3ra LATD

6 ..
8 5 d
2 4 *
8 3 '
8 2 ‘
..\° 1 ‘

0 _ l I I

512,102 1024,205 1280,512
XII er
2 — a kr1=kr2=2I kr1=kr2=3m LATD

U)
Q.
9
0
,,\°

 

 

512,102 1024,205 1280,512
XI] )32

Figure 11-17: Residual data accumulation and drop performance for different regional
data rates

In contrast, the trajectory determined by LATD has a lower trip collection-time while
achieving no residual data accumulation and low drops (0.1%). This is a direct result of
choosing the Navigation Agents only when there is enough data to utilize the contact time
with the MDH. This is brought out by the fact that the average hop count for LATD is

2.55 and the maximum hop count is 7 in comparison to an average hop count of 1.41 for

k’1 = k’2 = 2 and 1.66 for k’1 = k’2 = 3 . A side-effect of increase in the average hop

count is a corresponding increase in the energy per packet values as seen in Figure 11-18.

212

Thus, LATD effectively shrinks the trajectory in the low data rate region reducing the
overall trip collection-time and at the same time ensuring that it operates within the

stop-threshold.

100 ' a kr1=kr2=2 I kr1=kr2=3 1a LATD

 

Average Energy Per Packet
(mJ)

 

N A 0) oo
0 O O O O
I I 1 l 1

512,102 1024,205 1280,512
xrl kr2

24° ‘ a k"=k'2=2l kr1=kr2=30 LATD

SDRE

 

 

512,102 1024,205 1280,512
XI] )32

Figure 1 1-18: Energy metrics for different regional data rates

In the (1024, 205) bps case, the k’1 = k'2 = 2 trajectory is the same as the one for
(512,102) bps case as it uses data rate unaware NA selection. It is able to avoid residual
data accumulation and has slightly higher drops (0.4%). On the other hand,
k” = k’2 = 3 which results in a shorter trajectory continues to have high residual data
accumulation at the D65 and high drops (1.6%). Since this trajectory was beyond stop
threshold for a lower data rate combination of (512,102) bps, for the higher data rate,

213

k’1 = k'2 = 3 is also operating beyond the stop-threshold. In this case, the trajectory
determined by LATD has a trip collection-time comparable to k’1 = k’2 = 2 while
achieving no residual data accumulation and no drops. So, LATD is unable to shrink the
trajectory as much as in the lower data rate case of (512,102) bps. In effect, LATD

shrinks the trajectory slightly but ensures operation within the stop-threshold.

900-

g kr1=kr2=1 I kr1=kr2=2 U kr1=kr2=3 E LATD

Trip Collection-time (s)

 

 

256,341,171,102 512340341205 1024,1280,682,410
Ar1 A‘ZAfi‘Ar4 (bps)

Figure 11-19: Impact of regional data rates on trip collection-time
In the (1280, 512) bps or high data rate case, the k’1 = k'2 = 2 and k” = Ic’2 = 3
trajectories result in high residual data accumulation and drops suggesting that they are
beyond stop threshold. In this case, LATD trajectory is, in fact longer that both
k'1 = k’2 = 2 and k’1 = k’2 = 3. However, there is no residual data accumulation
along the trajectory and the drops are negligible. Thus, LAT D, for high data rate scenario
actually elongates the trajectory to ensure that it operates within the stop-threshold. This
is borne out by the higher trip collection-time and the average hop count numbers. The

average hop count for k’1 = k’2 = 2 and k’1 = k’2 = 3 is 1.39 and 1.6 respectively

214

while LAT'D has 1.19. Note that in this case, the energy metrics AEPP and SDRE also

benefit due to the longer trajectory and reduced hop counts as evidenced in Figure 1 1-18.

 

 

 

16 ‘ akfil=krz=1 - kn=kr2=2 u kn=kt2=3 ILATD
14 -
12 -
8
9 10 -
‘3“ 8 -
2
.\° 4 ‘
2 _
0 1 1 1
(a) 256,341,171,102 512,640,341,205 1024,1280,682,410
1.2 -

B kr1=kr2=1 I kfl=k|2=2 El kfl=k|2=3 LATD

 

 

256,341 ,171 ,102 512,640,341 ,205 1024,1280,682,410
A“ kr2kr3Xr4 (bp S)

(b)

Figure 11—20: Data accumulation and drops for varying regional data rates

Thus, based on the above cases, it is possible to conclude that LATD adapts

appropriately to the data rates of the regions and finds a trajectory that is able to operate

215

within the stop-threshold. In other words, the trajectory allows the MDH to collect data
without stopping.
11.6.2.2 Four-region Network Scenarios
120 -
a kfl=kfl=1 I k”=k"2=2 Ukr1=kr2=3 EJ LATD
100 -
80 -
60 r

40‘

AEPP (mJoule)

20'

 

 

256,341 ,171 ,102 512,640,341 ,205 1024,1280,682,410
krl 23223321“ (bpS)

A
m
v
_L
O)
O

" akr1=kt2=1 lk”=k'2=2 :1 kr1=kr2=3 GLATD
140 —

120 -

_L

O

O
I

Std. Deviation of Remaining
Energy
8

 

 

256,341,171,102 512,640,341 ,205 1024,1280,682,410
Figure 1 1-21: Energy metrics for varying regional data rates

Simulation experiments were conducted with a four region network with regional data

rates of (256,341,171,102), (512,640,341,205) and (1024,1280,682,410) respectively.
216

The duration of the collection cycle, I was set to 8008. For the single k scenario, the value
of k assigned for the four regions was chosen based on acceptable drop rates as
mentioned earlier for the two region cases. The single k values of l, 2 and 3 were
considered. The results obtained are presented in Figure 11-19 thru Figure 11-21.
Results show that LATD works in the same manner as observed in Section 11.6.2.1 and
adapts the trajectory to the regional data rates to ensure operation within the

stop-threshold.

11.7 Summary and Conclusions

In this Chapter, the problem of determining the extent of multi-hop routing for
networks with regionally differing data rates was considered. Potential problems of
using a single k value or data rate unaware trajectory determination were investigated.
Benefits of choosing the degree of multi-hop routing in a region specific manner were
outlined. Applying the insights gained fi'om the stability analysis in Chapters 10 and 11,
LATD (A-Aware Trajectory Determination) extensions were developed for the distributed
data collection framework, NADC proposed in Chapter 7. Performance of LATD was

evaluated using simulations and compared with the data rate unaware solution.

The analysis and the experimental results obtained in this chapter prove that there are
numerous benefits to choosing region specific multi-hop routing in heterogeneous data
rate networks. Performance benefits include stable operation, lower trip collection-time,
better energy and drop performance depending on the regional data rates. Results show
that the proposed distributed mechanism called LATD can effectively find a trajectory
that ensures that the data collection occurs within the stop-threshold and at the same time

minimize the trip collection-time. This is accomplished by identifying Navigation Agents

217

such that enough data has been buffered to use the contact time with the MDH as
efficiently as possible. In essence, LATD adapts the length of the trajectory based on the
data rates in the network. In higher data rate situations, the trajectory is elongated to
operate within stop-threshold and vice versa. In this chapter, the performance evaluation
of LATD shows that a distributed trajectory determination can successfully adapt to the
data rates and identify a trajectory that has good performance characteristics. Thus, the
work thus far provides proof of concept for distributed data rate aware trajectory

planning.

Ongoing work on the LATD algorithm has two main focal points. First, fine-tuning the
parameter 7 and investigating mechanisms to adapt it in real-time. Second, choosing the
NAs such that MAC layer contention is minimized to further improve the data collection

performance.

218

Chapter 12. Thesis Contributions and Future Work

12.1 Contributions

In this thesis, the role of controlled node mobility for enhancing data collection and
energy performance in wireless sensor networks was investigated. Joint mobility and
packet routing protocols were developed and the associated tradeoffs were analyzed from
a network design standpoint. Mobility algorithms and protocols were developed for two
types of deployments namely mobile sensor network with a static sink and static sensor
network with a mobile sink.

In Chapter 4, this thesis explores a paradigm for extending network life by introducing
energy-aware node mobility in wireless sensor networks. The concept of controlled
mobility for energy saving has been motivated by a natural grouping behavior that is
observed in Emperor penguins in the Antarctic region. In this thesis, a parallel was drawn
between the heat loss of a penguin at the group periphery, and the routing energy burden
of sensor nodes near the aggregating base stations in a sensor network. Then a fully
distributed and localized mobility control algorithm, and packet routing protocols were
developed for collective extension of the network life. Experimental results presented in
Chapter 5 demonstrate that the proposed controlled mobility framework can significantly
extend a sensor network’s operating life even in situations where the energy cost of
physical node movement is modeled as high as up to four orders of magnitude larger than
the energy cost for packet transmission.

From Chapter 6 onwards this thesis focuses on deployments with static sensors and a
mobile sink that collects data generated in the network. In Chapter 6, a generalized

framework based on hop-bound factor k to achieve a desired balance between trip

219

collection-time and energy consummion was proposed. This framework provides a joint
optimization of MDH trajectory and routing in the network. The idea is to choose a set of
DOS such that all nodes can route their data to one of the DOS within k hops. The value of
k has to be chosen in accordance with the application trip collection-time and lifetime
requirements. In addition, a model was developed for the generalized framework and
analyzed. The analysis showed that hop-bound k can be used as a knob to balance the trip
collection-time and energy consumption.

The stability analysis presented in Chapter 9 revealed that for given network and MDH
parameters such as data generation rate A, MDH speed v, collection cycle 1:, link capacity
C, there exist hop-bound thresholds that define the feasible range of k for data collection.

Feasible ranges were identified for sinks that can stop (Stop-on-Demand mode) and

cannot stop (Collect-on-Move mode). For both modes, the stability threshold, ks,_10w

dictates the lower bound of k below which the trip collection-time t would be

coll

greater than 1 making the system unstable. The stop threshold, ks on the other hand

top
determines the point beyond which the MDH has to incorporate stops into its trajectory to

complete data collection from the DOS. Therefore, ks indicates the upper-bound for

top
Collect-on-Move data collection. This threshold impacts the choice of sink technology as
pointed out in Section 6.6.2.

In Chapter 9, stability analysis was performed assuming that the nodal data rates
were significantly lower than the MDH-DG links. In Chapter 10, the low data rate
assumption was relaxed. The stability analysis was repeated with taking into account the

MDH-DG link capacity and the impact of interference of the transmissions around the

220

MDH. The analysis showed that a stability-ceiling, ks,_ high exists which upper-bounds

the feasible region of k.

The energy-trip collection-time tradeoff and the thresholds predicted by the model were
experimentally validated by developing a Network-Assisted Data Collection (NADC) as
described in Chapters 7 and 8, which is a distributed and location-unaware sink
navigation and data routing protocol. Finally, the NADC framework was extended for
networks with spatially heterogeneous data generation rates in Chapter 11, and the

routing feasibility results were validated for such scenarios.

12.2 Future Work

The work undertaken during this thesis has opened up a number of new avenues for
firture research. In the context of data gathering, the trajectory of the sink impacts the trip
time and extent of routing required for packet delivery which in turn affect performance

metrics such as packet delay, delivery rate, energy efficiency and network lifetime.

1) Multiple Mobile Sinks

One way to increase the feasibility range for multi-hop routing (k) is to employ multiple
mobile sinks. This raises the problem of trajectory planning for multiple sinks such that
they collect data from the network in a load-balanced way. Thus far, in the literature, sink
trajectories [112, 153] are pre-prograrnmed and thus are unable to adapt to dynamic
changes in the network. Alternatively, the mobile sink randomly determines the next
location to move to in real-time based on local information which has the disadvantage of
sub—optimal decisions and unbounded delays. Distributed network assisted trajectory
planning approach has the distinct advantage of being able to adapt to topological

dynamics, changes in data generation profile or altered mission goals and offer a bounded

221

delay. A number of design extensions would need to be developed for NADC (Network
Assisted Data Collection) to handle multiple sinks. Trajectory planning solutions will be
required for different scenarios emerging from the connectivity that is assumed among

the sinks themselves.

2) Under Water Sensor Network Environments

Recently sensor networks deployed in underwater environments [154-156] have
started to receive a lot of attention for oceanographic, assisted navigation, pollution
monitoring and such other applications. Like the terrestrial networks, underwater sensors
are also energy constrained. In addition, these networks face a number of challenges
including low data rate, long propagation delays, unidirectional links, unpredictable link
characteristics and constant mobility even of anchored nodes which float due to the
currents. Trajectory planning for mobile sinks in such networks is a complex task.
Centralized approaches to trajectory computation are not readily applicable here due to
the dynamic environment. Distributed mechanisms have an inherent advantage since they
can adapt to changing conditions. We plan to apply the insights gained from the thesis to

sink path planning problem in underwater networks.

222

APPENDIX A: Numerical Analysis of Feasibility Range for Full

Coverage Scenario
In Chapters 6 and 10, analysis was performed using 55 = 2kr which is the separation
between the circular sub-trajectories. This separation distance was chosen to ensure no
overlaps in coverage for ensuring tractability of energy analysis. However, the analysis
holds for any value of 65 . In this section, we present the results obtained by repeating the
analysis with 53 = «Ekr to ensure complete area coverage [77]. The equation

modifications and the updated graphs are presented in this section.

A.1 Trajectory Length (L)

J36

The radius of the outermost sub-traj ectory is R-—2— , and adjacent sub-traj ectories are

mutually separated by a distance of J— 3kr. Thus, the radius of the I" sub-trajectory (starting

,/'

from the periphery) = R'[ -23kr+(i—l)\/3kr] for i=1 to R/‘Ekr , and the

circumference is 27r[R — (kr(2i —1))]. The total trajectory length L can be written as:
[R/ er/g )1
L = 271 (Z [R — (kn/3 (1' — %))]+ 211,,
i=1

where Rm represents the transition distance (see Figure 6-5) fi'om the outer-most to the
inner-most sub-trajectory. The quantity 2R", represents the distances traversed from one
sub-trajectory to the next during a complete trip.

A.2 Update Time (Tu )

The contact distance with at least one DG in the vicinity of a Navigation Agent can range

223

from 2r (when a Navigation Agent and all its DGs are collocated) to 2J3r (when at least
two DGs of a Navigation Agent are located on two diametrically opposite points on the
circle of radius r around the Navigation Agent). With uniformly dense node distribution,

the contact period can be assumed to be symmetric around the midpoint of the contact
distance range (Zr and 2J3r) leading to an average of (r + fir) . The data forwarded by

the interfering nodes is essentially all the data generated in the network except the data

generated by the DOS themselves. Thus, we have:

Cin =(r+\/3r){ [7t(kT)ZAd—W2/id]= [2.73x70'3/1d(k2 —1Jv/

Substituting the value of C11. , the upload time is computed as:

1,,=1.1.2..y(e-(.1.x.swz41»

A.3 Numerical Evaluation
The evaluation presented in Section 10.4 was repeated with the modified equations above.

The results obtained are shown in the figures below.

224

Unstable region

Stable region

Unstable region

Stable Region

 

 

 

 

 

4 5 6 7 8 9 10
Hop-bound k

Figure A-l: Impact of k on trip collection-time for low data rates

r 2000

A
U)

V

LO
0
-0—'

 

1800
1600
1400
1200
1000

800

600 ~

400
200
0

1

1

 

    

A=184 bps

 
   

Desirabl
k range

1 2

3

I : Feasible

e P’ ,l 31’ krange

I I I I I T I

4 5 6 7 8 9 10
Hop—boundk

Figure A-2: of k on trip collection-time for higher data rates

225

 

 

Stable Region Unstajltle region

 

 

Hop-bound k

Figure A-3: Impact ofkonTm, Tp and Tu

 

 

 

Degree of Multi-hop Routing (k)

 

3 J kst_low

 

 

I I I I I

20 70 120 170 220 270 320
A(bps)

Figure A-4: Impact of data rate on thresholds

226

370

 

 

 

 

Degree of Multi-hop Routing (k)

 

1 I I r I I I I
0 1 2 3 4 5 6 7
Nodes/100 In2

Figure A-S: Impact of density on thresholds

 

10 ‘ kst_high '

7 q kstop

 

 

 

kst_low

I I I I I

500 1000 1500 2000 2500 3000 3500
Capacity of MDH-DG links (kbps)

 

Degree of Multi-hop Routing (k)

Figure A-6: Impact of MDH-DG link capacity on thresholds

227

 

 

 

 

:12 kst_high

v \

.810 ‘ '

‘8 _

m 8

o- I

g 6 a kstop

E 4 -

“6

92’ 2 ‘ kst_Iow

D D

8 0 I I I I I I I I l I I I I
123456789101112131415

MDH speed (m/s)

Figure A-7: Impact of speed on thresholds

A.4 Conclusions

The results presented in this section show that although the absolute values are slightly
different, the overall trends for the trip collection-time and the stability thresholds are the
same as those observed in Chapter 10. Thus, irrespective of value of the coverage

parameter a, , the analysis presented in Chapters 7, 9 and 10 hold.

228

APPENDIX B: Length of Sample Covering Trajectories

The trajectory length L depends on the covering trajectory and can be calculated as

shown below for the sample trajectories discussed in Chapter 6.

 

a)

Concentric circular

 

 

 

 

 

Spiral

 

 

 

R/2kr
L: 27: Z[R—(kr(2i—1))]+2Rm

 

L=2R/25S {JRZ— [R— 65(2i—1)]2—65}
1

i:

37: d
+T{R—73}+2Rm2

2
2 5

 

 

Figure B-l: Trajectory length computation for sample covering trajectories

229

 

[ll

[2]

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