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Title: Collision-free communication in sensor networks
Creator: Arumugam, Umamaheswaran
Date: 2003
Collection: Electronic Theses & Dissertations

Title: Signal processing and machine learning approaches to enabling advanced sensing and networking capabilities in everyday infrastructure and electronics
Creator: Ali, Kamran (Scientist)
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Mainstream commercial off-the-shelf (COTS) electronic devices of daily use are usually designed and manufactured to serve a very specific purpose. For example, the WiFi routers and network interface cards (NICs) are designed for high speed wireless communication, RFID readers and tags are designed to identify and track items in supply chain, and smartphone vibrator motors are designed to provide haptic feedback (e.g. notifications in silent mode) to the users. This dissertation focuses on...
Show moreMainstream commercial off-the-shelf (COTS) electronic devices of daily use are usually designed and manufactured to serve a very specific purpose. For example, the WiFi routers and network interface cards (NICs) are designed for high speed wireless communication, RFID readers and tags are designed to identify and track items in supply chain, and smartphone vibrator motors are designed to provide haptic feedback (e.g. notifications in silent mode) to the users. This dissertation focuses on revisiting the physical-layer of various such everyday COTS electronic devices, either to leverage the signals obtained from their physical layers to develop novel sensing applications, or to modify/improve their PHY/MAC layer protocols to enable even more useful deployment scenarios and networking applications - while keeping their original purpose intact - by introducing mere software/firmware level changes and completely avoiding any hardware level changes. Adding such new usefulness and functionalities to existing everyday infrastructure and electronics has advantages both in terms of cost and convenience of use/deployment, as those devices (and their protocols) are already mainstream, easily available, and often already purchased and in use/deployed to serve their mainstream purpose of use.In our works on WiFi signals based sensing, we propose signal processing and machine learning approaches to enable fine-grained gesture recognition and sleep monitoring using COTS WiFi devices. In our work on gesture recognition, we show for the first time thatWiFi signals can be used to recognize small gestures with high accuracy. In our work on sleep monitoring, we propose for the first time aWiFi CSI based sleep quality monitoring scheme which can robustly track breathing and body/limb activity related vital signs during sleep throughout a night in an individual and environment independent manner.In our work on RFID signals based sensing, we propose signal processing and machine learning approaches to effectively image customer activity in front of display items in places such as retail stores using commercial off-the-shelf (COTS) monostatic RFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags). The key novelty of this work is on achieving multi-person activity tracking in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging. We implemented our scheme using a COTS RFID reader and tags.In our work on smartphone's vibration based sensing, we propose a robust and practical vibration based sensing scheme that works with smartphones with different hardware, can extract fine-grained vibration signatures of different surfaces, and is robust to environmental noise and hardware based irregularities. A useful application of this sensing is symbolic localization/tagging, e.g. figuring out whether a user's device is in their hand, pocket, or at their bedroom table, etc. Such symbolic tagging of locations can provide us with indirect information about user activities and intentions without any dedicated infrastructure, based on which we can enable useful services such as context aware notifications/alarms. To make our scheme easily scalable and compatible with COTS smartphones, we design our signal processing and machine learning pipeline such that it relies only on builtin vibration motors and microphone for sensing, and it is robust to hardware irregularities and background environmental noises. We tested our scheme on two different Android smartphones.In our work on powerline communications (PLCs), we propose a distributed spectrum sharing scheme for enterprise level PLC mesh networks. This work is a major step towards using existing COTS PLC devices to connect different types of Internet of Things (IoT) devices for sensing and control related applications in large campuses such as enterprises. Our work is based on identification of a key weakness of the existing HomePlug AV (HPAV) PLC protocol that it does not support spectrum sharing, i.e., currently each link operates over the whole available spectrum, and therefore, only one link can operate at a time. Our proposed spectrum sharing scheme significantly boosts both aggregated and per-link throughputs, by allowing multiple links to communicate concurrently, while requiring a few modifications to the existing HPAV protocol.
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Title: Energy efficient reprogramming for sensor networks
Creator: Wang, Limin
Date: 2007
Collection: Electronic Theses & Dissertations

Title: Planning and control of mobile surveillance networks
Creator: Goradia, Amit
Date: 2006
Collection: Electronic Theses & Dissertations

Title: Improving data transmission reliability and throughput in wireless sensor networks
Creator: Lee, Ee Foong
Date: 2008
Collection: Electronic Theses & Dissertations

Title: Spatio-temporal field prediction under localization uncertainty for mobile sensor networks
Creator: Jadaliha, Mahdi
Date: 2013
Collection: Electronic Theses & Dissertations
Description: Recently, there has been a growing interest in wireless sensor networks due to the advanced embedded system and network technologies. Their applications include, but are not limited to, environment monitoring, building comfort control, traffic control, manufacturing and plant automation, and surveillance systems. The conventional inverse problem approach based on physical transport models is computationally prohibitive for resource-constrained, multi-agent systems. In contrast, Gaussian...
Show moreRecently, there has been a growing interest in wireless sensor networks due to the advanced embedded system and network technologies. Their applications include, but are not limited to, environment monitoring, building comfort control, traffic control, manufacturing and plant automation, and surveillance systems. The conventional inverse problem approach based on physical transport models is computationally prohibitive for resource-constrained, multi-agent systems. In contrast, Gaussian process and Gaussian Markov models have been widely used to draw statistical inference from geostatistical and environmental data. However, the statistical models need to be carefully tailored such that they can be practical and usable for mobile sensor networks with limited resources. In addition, reducing localization uncertainty in low-cost mobile sensors is very challenging. Thus, a fundamental problem in various applications is to correctly fuse the spatially collected data and estimate the process of interest under localization uncertainty.Motivated by the aforementioned issues, in this dissertation, we consider the problem of using mobile sensor networks to estimate and predict environmental fields modeled by spatio-temporal Gaussian processes and Gaussian Markov random fields in the presence of localization uncertainty.In the first part of this dissertation, we formulate Gaussian process regression with observations under the localization uncertainty. In our formulation, effects of measurement noise, localization uncertainty, and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically intractable posterior predictive statistics are proposed to be approximated by two techniques, viz., Monte Carlo sampling and Laplace's method. In addition, the localization problem is studied in this part, when the position of the robot is estimated by a maximum likelihood estimation (MLE) using vision data. We transform the high dimensional vision data to a set of uncorrelated feature candidates. A multivariate GP regression with unknown hyperparameters is formulated to connect the set of selected features to their corresponding sampling positions. In order to decrease computational load and increase the accuracy of localization, a feature reduction approach is developed. Therefore, a subset of the features is selected to minimize the localization error using cross-validation methods.In the second part of the dissertation, we consider the problem of predicting a spatial (spatio-temporal) random field using sequential noisy observations collected by robotic sensors. The random field of interest is modeled by a Gaussian Markov random field (GMRF) instead of Gaussian process. In this way, we proposed iteratively updated predictive inferences. We derive the exact Bayesian solution to the problem of computing the predictive inference of the random field, taking into account uncertain hyperparameters, measurement noise, and uncertain localization in a fully Bayesian point of view. We show that the exact solution is not scalable as the number of observations increases. To cope with this exponentially increasing complexity, we propose scalable approximations with a controllable tradeoff between approximation error and complexity to the exact solution. Finally, we derive an approximate Bayesian solution to the problem of the simultaneously localization and computing the predictive inferences, taking into account observations, uncertain hyperparameters, measurement noise, kinematics of robotic sensors, and uncertain localization.
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Title: Rapid prototyping and quick deployment of sensor networks
Creator: Arumugam, Umamaheswaran
Date: 2006
Collection: Electronic Theses & Dissertations

Title: Controlled mobility for performance enhancements in wireless sensor networks
Creator: Rao, Jayanthi
Date: 2009
Collection: Electronic Theses & Dissertations

Title: Self organization in medium access control for wireless ad hoc and sensor networks
Creator: Yu, Fan
Date: 2008
Collection: Electronic Theses & Dissertations

Title: Tackling the challenges of wireless interference and coexistence
Creator: Huang, Jun
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Recent years have witnessed the phenomenal penetration rate of wireless networks in our daily lives, ranging from 802.11-based wireless LANs that provide ubiquitous Internet access, to 802.15.4-based wireless sensor networks that carry out various mission-critical tasks such as security surveillance and patient monitoring. However, despite the advances in the field of wireless networking, how to design high-performance wireless networks remains an open problem because of the fundamental...
Show moreRecent years have witnessed the phenomenal penetration rate of wireless networks in our daily lives, ranging from 802.11-based wireless LANs that provide ubiquitous Internet access, to 802.15.4-based wireless sensor networks that carry out various mission-critical tasks such as security surveillance and patient monitoring. However, despite the advances in the field of wireless networking, how to design high-performance wireless networks remains an open problem because of the fundamental challenges of wireless interference and coexistence.Interference is the fundamental factor that limits the link concurrency of wireless networks. Due to the broadcast nature of wireless channel, concurrent transmissions on the same frequency interfere with each other over the air, resulting in lower throughput and higher delivery delay. Handling interference in wireless networks is difficult because of the hidden terminal problem and the exposed terminal problem. Although the former is well studied in existing literature, the later is not, especially in networks where multiple bit rates are available.Another challenge is that interference significantly hinders the coexistence of different wireless technologies. In particular, recent studies show that wireless coexistence is becoming a growing issue due to the unprecedented proliferation of wireless devices in the unlicensed 2.4GHz band. When devices of heterogeneous physical layer operating on the same frequency, interference is more difficult to resolve as devices cannot decode the signals of each other. Moreover, co-existing devices commonly transmit at different powers, which leads to unfair channel usage. The issue is particularly critical to lower power wireless devices. This thesis tackles the fundamental challenges of wireless interference and coexistence to the link layer design of wireless networks. In particular, we identify two problems in the design of existing link layer protocols, and advance the state-of-the-art by offering practical solutions: (1) the rate-adaptive exposed terminal problem where link concurrency cannot be fully exploited by conventional link layers because of they are oblivious to the bit rate diversity; and (2) the blind terminal problem where existing link layer protocols fail to work in co-existing environments due to the heterogeneous physical layer and power asymmetry of co-existing devices. We motivate this research by showing that existing link layer protocols are surprisingly ineffective in handling these problems. Our experiments pinpoint the fundamental reasons of such ineffectiveness and reveal their implications on the design of link layer protocols. We then develop practical solutions to tackle the identified problems. Extensive testbed-based experiments validate the design of proposed solutions, and demonstrate their significant performance gains over existing link layer protocols.
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Title: Adapting wireless sensor networks to obstructed and concave environments
Creator: Wang, Chen
Date: 2007
Collection: Electronic Theses & Dissertations

Title: Exploiting cross-technology interference for efficient network services in wireless systems
Creator: Zhou, Ruogu
Date: 2014
Collection: Electronic Theses & Dissertations
Description: In the last decade, we have witnessed the wide adoption of a variety of wireless technologies like WiFi, Cellular, Bluetooth, ZigBee, and Near-field Communication(NFC). However, the fast growth of wireless networks generates significant cross-technology interference, which leads to network performance degradation and potential security breach. In this dissertation, we propose two novel physical layer techniques to deal with the interference, and improve the performance and security of sensor...
Show moreIn the last decade, we have witnessed the wide adoption of a variety of wireless technologies like WiFi, Cellular, Bluetooth, ZigBee, and Near-field Communication(NFC). However, the fast growth of wireless networks generates significant cross-technology interference, which leads to network performance degradation and potential security breach. In this dissertation, we propose two novel physical layer techniques to deal with the interference, and improve the performance and security of sensor networks and mobile systems, respectively. First, we exploit the WiFi interference as a ``blessing" in the design of sensor networks and develop novel WiFi interference detection techniques for ZigBee sensors. Second, utilizing these techniques, we design three efficient network services: WiFi discovery which detects the existence of nearby WiFi networks using ZigBee sensors, WiFi performance monitoring which measures and tracks performance of WiFi networks using a ZigBee sensor network, and time synchronization which provides synchronized clocks for sensor networks based on WiFi signals. Third, we design a novel, noninvasive NFC security system called {\em nShield} to reduce the transmission power of NFC radios, which protects NFC against passive eavesdropping. nShield implements a novel adaptive RF attenuation scheme, in which the extra RF energy of NFC transmissions is determined and absorbed by nShield. At the same time, nShield scavenges the extra RF energy to sustain the perpetual operation. Together with the extremely lo-power design, it enables nShield to provide the host uninterrupted protection against malicious eavesdropping. The above systems are implemented and extensively evaluated on a testbed of sensor networks and smartphones.
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Title: Spatial diversity in wireless sensor networks
Creator: Devarakonda, Sivanvitha
Date: 2007
Collection: Electronic Theses & Dissertations

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