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Title: Unobtrusive physiological monitoring using smartphones
Creator: Hao, Tian (Research scientist)
Date: 2015
Collection: Electronic Theses & Dissertations
Description: "This thesis presents an in-depth investigation in unobtrusive smartphone-based physiological monitoring, which aims to help people get healthier and fitter in a more efficient and less costly way." -- Abstract.

Title: Smartphone-based sensing systems for data-intensive applications
Creator: Moazzami, Mohammad-Mahdi
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "Supported by advanced sensing capabilities, increasing computational resources and the advances in Artificial Intelligence, smartphones have become our virtual companions in our daily life. An average modern smartphone is capable of handling a wide range of tasks including navigation, advanced image processing, speech processing, cross app data processing and etc. The key facet that is common in all of these applications is the data intensive computation. In this dissertation we have taken...
Show more"Supported by advanced sensing capabilities, increasing computational resources and the advances in Artificial Intelligence, smartphones have become our virtual companions in our daily life. An average modern smartphone is capable of handling a wide range of tasks including navigation, advanced image processing, speech processing, cross app data processing and etc. The key facet that is common in all of these applications is the data intensive computation. In this dissertation we have taken steps towards the realization of the vision that makes the smartphone truly a platform for data intensive computations by proposing frameworks, applications and algorithmic solutions. We followed a data-driven approach to the system design. To this end, several challenges must be addressed before smartphones can be used as a system platform for data-intensive applications. The major challenge addressed in this dissertation include high power consumption, high computation cost in advance machine learning algorithms, lack of real-time functionalities, lack of embedded programming support, heterogeneity in the apps, communication interfaces and lack of customized data processing libraries. The contribution of this dissertation can be summarized as follows. We present the design, implementation and evaluation of the ORBIT framework, which represents the first system that combines the design requirements of a machine learning system and sensing system together at the same time. We ported for the first time off-the-shelf machine learning algorithms for real-time sensor data processing to smartphone devices. We highlighted how machine learning on smartphones comes with severe costs that need to be mitigated in order to make smartphones capable of real-time data-intensive processing. From application perspective we present SPOT. SPOT aims to address some of the challenges discovered in mobile-based smart-home systems. These challenges prevent us from achieving the promises of smart-homes due to heterogeneity in different aspects of smart devices and the underlining systems. We face the following major heterogeneities in building smart-homes:: (i) Diverse appliance control apps (ii) Communication interface, (iii) Programming abstraction. SPOT makes the heterogeneous characteristics of smart appliances transparent, and by that it minimizes the burden of home automation application developers and the efforts of users who would otherwise have to deal with appliance-specific apps and control interfaces. From algorithmic perspective we introduce two systems in the smartphone-based deep learning area: Deep-Crowd-Label and Deep-Partition. Deep neural models are both computationally and memory intensive, making them difficult to deploy on mobile applications with limited hardware resources. On the other hand, they are the most advanced machine learning algorithms suitable for real-time sensing applications used in the wild. Deep-Partition is an optimization-based partitioning meta-algorithm featuring a tiered architecture for smartphone and the back-end cloud. Deep-Partition provides a profile-based model partitioning allowing it to intelligently execute the Deep Learning algorithms among the tiers to minimize the smartphone power consumption by minimizing the deep models feed-forward latency. Deep-Crowd-Label is prototyped for semantically labeling user's location. It is a crowd-assisted algorithm that uses crowd-sourcing in both training and inference time. It builds deep convolutional neural models using crowd-sensed images to detect the context (label) of indoor locations. It features domain adaptation and model extension via transfer learning to efficiently build deep models for image labeling. The work presented in this dissertation covers three major facets of data-driven and compute-intensive smartphone-based systems: platforms, applications and algorithms; and helps to spurs new areas of research and opens up new directions in mobile computing research."--Pages ii-iii.
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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: Energy efficient object detection and measurement for smart glasses
Creator: Yang, Jing
Date: 2014
Collection: Electronic Theses & Dissertations
Description: We design and implement a novel object detection and measurement system called Lockon for smart glasses. Lockon takes the advantages of the mounting position of the smart glasses, and provides users two useful functions that can benefit a wide range of applications. Lockon can accurately locate the object of interest (OoI) in the view of the user and inform the user the position of the OoI in real-time, using the front-facing camera equipped on the smart glasses and advanced computer vision...
Show moreWe design and implement a novel object detection and measurement system called Lockon for smart glasses. Lockon takes the advantages of the mounting position of the smart glasses, and provides users two useful functions that can benefit a wide range of applications. Lockon can accurately locate the object of interest (OoI) in the view of the user and inform the user the position of the OoI in real-time, using the front-facing camera equipped on the smart glasses and advanced computer vision and image processing techniques. To conserve energy, Lockon implements a motion trigger to intelligently activate the object detection process only when it is necessary. Lockon can also accurately measure the dimension of the object, with a 3D ranging technique. This capability allows user to remotely estimate the dimension of the object. We implement Lockon on Google Glass Explorer Edition, and evaluate the performance of Lockon using extensive experiments. Our results indicate that Lockon can achieve high detection accuracy (0.95 true positive rate and $4.3 \times 10^{-7}$ false positive rate), low object dimension measurement error (3.3\% when distance is less than 1 m), and low delay (300 ms).
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Title: Aquatic environment monitoring using robotic sensor networks
Creator: Wang, Yu, Ph. D.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Aquatic environment has been facing an increasing number of threats from various harmful aquatic processes such as oil spills, harmful algal blooms (HABs), and aquatic debris. These processes greatly endanger the aquatic ecosystems, marine life, human health, and water transport. Hence, it is of great interest to detect these processes and monitor their evolution such that proper actions can be taken to prevent the potential risks. This dissertation explores four representative problems in...
Show moreAquatic environment has been facing an increasing number of threats from various harmful aquatic processes such as oil spills, harmful algal blooms (HABs), and aquatic debris. These processes greatly endanger the aquatic ecosystems, marine life, human health, and water transport. Hence, it is of great interest to detect these processes and monitor their evolution such that proper actions can be taken to prevent the potential risks. This dissertation explores four representative problems in aquatic environment monitoring, which include diffusion processing profiling, spatiotemporal field reconstruction, aquatic debris surveillance, and water surface monitoring.First, we propose an accuracy-aware approach to profiling an aquatic diffusion process such as oil spill. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including its source location, discharged substance amount, and evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to limited sensor mobility and energy budget. To solve this problem, we develop an efficient gradient-ascent-based algorithm and a near-optimal dynamic-programming-based algorithm.Second, we present a novel approach to reconstructing a spatiotemporal aquatic field such as HABs. This approach features a rendezvous-based mobility control scheme where robotic sensors collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Moreover, information-theoretic analysis is used to guide the selection of rendezvous regions so that the reconstruction accuracy is maximized subject to the limited sensor mobility.Third, we develop a vision-based, cloud-enabled, low-cost, yet intelligent solution to aquatic debris surveillance. Our approach features real-time debris detection and coverage-based rotation scheduling algorithms. Specifically, the image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environment, e.g.,, constant camera shaking due to waves. The rotation scheduling algorithm provides effective coverage of sporadic debris arrivals despite camera's limited angular view. Moreover, we design a dynamic task offloading scheme to offload the computation-intensive processing tasks to the cloud for battery power conservation.Finally, we design Samba -- an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish for general water surface monitoring. Using the built-in camera of on-board smartphone, Samba can detect spatially dispersed aquatic processes. To reduce the excessive false alarms caused by the non-water area, Samba segments the captured images and performs target detection in the identified water area only. We propose a novel approach that leverages the power-efficient inertial sensors on smartphone to assist the image processing. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features.
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Title: Design and deployment of low-cost wireless sensor networks for real-time event detection and monitoring
Creator: Phillips, Dennis Edward
Date: 2018
Collection: Electronic Theses & Dissertations
Description: As sensor network technologies become more mature, they are increasingly being applied to a wide variety of environmental monitoring applications, ranging from agricultural sensing to habitat monitoring, oceanic and volcanic monitoring. In this dissertation two wireless sensor networks (WSNs) are presented. One for monitoring residential power usage and another for producing an image of a volcano's internal structure.The two WSNs presented address several common challenges facing modern...
Show moreAs sensor network technologies become more mature, they are increasingly being applied to a wide variety of environmental monitoring applications, ranging from agricultural sensing to habitat monitoring, oceanic and volcanic monitoring. In this dissertation two wireless sensor networks (WSNs) are presented. One for monitoring residential power usage and another for producing an image of a volcano's internal structure.The two WSNs presented address several common challenges facing modern sensor networks. The first is in-network processing and assigning the processing tasks across a heterogeneous network architecture. By efficiently utilizing in-network processing power consumption can be reduced and operational lifetime of the network can be extended. As nodes are embedded into various environments sensing accuracy is intrinsically affected by physical noise. The second challenge relates to how to deal with this noise in a way which increases sensing accuracy. The third challenge is ease of deployment. As WSNs become more common place they will be installed by non-experts.As a key technology of home area networks in smart grids, fine-grained power usage monitoring may help conserve electricity. Smart homes outfitted with network connected appliances will provide this capability in the future. Until smart appliances have wide adaption there is a serious gap in capabilities. To fill this gap an easy to deploy monitoring system is needed. Several existing systems achieve the goal of fine-grained power monitoring by exploiting appliances' power usage signatures utilizing labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in high installation cost. Supero is the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multi-sensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, non-professional users can quickly deploy Supero with considerable flexibility.There are a number of active volcanos around the world with large population areas located nearby. An eruption poses a significant threat to the adjacent population. During times of increased activity being able to obtain a real-time images of the interior would allow seismologists to better understand volcanic dynamics. Volcano tomography can provide this valuable information concerning the internal structure of a volcano. The second sensor network presented in this dissertation is a seismic monitoring sensor network featuring in-network processing of the seismic signals with the capability to perform volcano tomography in real-time. The design challenges, analysis of processing/network processing times in the information processing pipeline, the system designed to meet these challenges and the results from deploying a prototype network on two volcanoes in Ecuador and Chile are presented. The study shows that it is possible to achieve in-network seismic event detection and real-time tomography using a sensor network that is 2 orders of magnitude less expensive than traditional seismic equipment.
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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: Holistic performance control for mission-critical cyber-physical systems
Creator: Chen, Jinzhu
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Recent years have seen the growing deployments of Cyber-Physical Systems (CPSs) in many mission-critical applications such as security, civil infrastructure, and transportation. These applications often impose stringent performance requirements on system sensing fidelity, timeliness, energy efficiency and reliability. However, existing approaches treat these concerns in isolation and hence are not suitable for CPSs where the...
Show moreRecent years have seen the growing deployments of Cyber-Physical Systems (CPSs) in many mission-critical applications such as security, civil infrastructure, and transportation. These applications often impose stringent performance requirements on system sensing fidelity, timeliness, energy efficiency and reliability. However, existing approaches treat these concerns in isolation and hence are not suitable for CPSs where the system performances are dependent of each other because of the tight integration of computational and physical processes. In this dissertation, we investigate the dependencies between these performances and propose the holistic performance control approaches for two typical mission-critical CPSs, which are Wireless Cyber-phyiscal Surveillance (WCS) systems and data centers. We first propose a holistic approach called {\em Fidelity-Aware Utilization Controller} (FAUC) for WCS systems that combine low-end sensors with cameras for large-scale ad hoc surveillance in unplanned environments. By integrating data fusion with feedback control, FAUC enforces a CPU utilization upper bound to ensure the system's real-time schedulability under dynamic CPU workloads at runtime because of stochastic detection results. At the same time, FAUC optimizes system fidelity and adjusts the control objective of CPU utilization adaptively in the presence of variations of target/noise characteristics. The testbed experiments and the trace-driven simulations show that FAUC can achieve robust fidelity and real-time guarantees in dynamic environments.We then present a proactive thermal and energy control approach for data centers to improve the energy efficiency while ensuring the data center reliability. It consists of a high-fidelity real-time temperature prediction system and a predictive thermal and energy control (PTEC) system. The prediction system integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing and real-time data-driven prediction. To ensure the forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. Based on the temperature prediction system, we propose the PTEC system, which leverages the server built-in sensors and monitoring utilities, as well as a network of wireless sensors to monitor the thermal and power conditions of a data center. It predicts the server inlet temperatures in real-time, and optimizes temperature setpoints and cold air supply rates of cooling systems, as well as the speeds of server internal fans, to minimize their overall energy consumption. To ensure the data center reliability, PTEC enforces a set of thermal safety requirements including the upper bounds on server inlet temperatures and their variations, to prevent server overheating and reduce server hardware failure rate. A partition-based approach is proposed to solve the control problem efficiently for large-scale data centers. Extensive testbed experiments and trace-driven CFD simulations show that PTEC can safely reduce substantial cooling and circulation energy consumption compared with traditional approaches, and can adapt to the realistic and dynamic data center workload.
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Title: Harnessing low-pass filter defects for improving wireless link performance : measurements and applications
Creator: Renani, Alireza Ameli
Date: 2018
Collection: Electronic Theses & Dissertations
Description: "The design trade-offs of transceiver hardware are crucial to the performance of wireless systems. The effect of such trade-offs on individual analog and digital components are vigorously studied, but their systemic impacts beyond component-level remain largely unexplored. In this dissertation, we present an in-depth study to characterize the surprisingly notable systemic impacts of low-pass filter design, which is a small yet indispensable component used for shaping spectrum and rejecting...
Show more"The design trade-offs of transceiver hardware are crucial to the performance of wireless systems. The effect of such trade-offs on individual analog and digital components are vigorously studied, but their systemic impacts beyond component-level remain largely unexplored. In this dissertation, we present an in-depth study to characterize the surprisingly notable systemic impacts of low-pass filter design, which is a small yet indispensable component used for shaping spectrum and rejecting interference. Using a bottom-up approach, we examine how signal-level distortions caused by the trade-offs of low-pass filter design propagate to the upper-layers of wireless communication, reshaping bit error patterns and degrading link performance of today's 802.11 systems. Moreover, we propose a novel unequal error protection algorithm that harnesses low-pass filter defects for improving wireless LAN throughput, particularly to be used in forward error correction, channel coding, and applications such as video streaming. Lastly, we conduct experiments to evaluate the unequal error protection algorithm in video streaming, and we present substantial enhancements of video quality in mobile environments."--Page ii.
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Title: Capturing bluetooth traffic in the wild : practical systems and privacy implications
Creator: Albazrqaoe, Wahhab
Date: 2018
Collection: Electronic Theses & Dissertations
Description: "Bluetooth wireless technology is today present in billions of smartphones, mobile devices, and portable electronics. With the prevalence of personal Bluetooth devices, a practical Bluetooth traffic sniffer is of increasing interest due to the following. First, it has been reported that a traffic sniffer is an essential, day-to-day tool for Bluetooth engineers and applications developers [4] [14]; and second, as the communication between Bluetooth devices is privacy-sensitive in nature,...
Show more"Bluetooth wireless technology is today present in billions of smartphones, mobile devices, and portable electronics. With the prevalence of personal Bluetooth devices, a practical Bluetooth traffic sniffer is of increasing interest due to the following. First, it has been reported that a traffic sniffer is an essential, day-to-day tool for Bluetooth engineers and applications developers [4] [14]; and second, as the communication between Bluetooth devices is privacy-sensitive in nature, exploring the possibility of Bluetooth traffic sniffing in practical settings sheds lights into potential user privacy leakage. To date, sniffing Bluetooth traffic has been widely considered an extremely intricate task due to wideband spread spectrum of Bluetooth, pseudo-random frequency hopping adopted by Bluetooth at baseband, and the interference in the open 2.4 GHz band. This thesis addresses these challenges by introducing novel traffic sniffers that capture Bluetooth packets in practical environments. In particular, we present the following systems. (i) BlueEar, the first practical Bluetooth traffic sniffing system only using general, inexpensive wireless platforms. BlueEar features a novel dual-radio architecture where two inexpensive, Bluetooth-compliant radios coordinate with each other to eavesdrop on hopping subchannels in indiscoverable mode. Statistic models and lightweight machine learning tools are integrated to learn the adaptive hopping behavior of the target. Our results show that BlueEar maintains a packet capture rate higher than 90% consistently in dynamic settings. In addition, we discuss the implications of the BlueEar approach on Bluetooth LE sniffing and present a practical countermeasure that effectively reduces the packet capture rate of sniffer by 70%, which can be easily implemented on the Bluetooth master while requiring no modification to slave devices like keyboards and headsets. And (ii) BlueFunnel, the first low-power, wideband traffic sniffer that monitors Bluetooth spectrum in parallel and captures packet in realtime. BlueFunnel tackles the challenge of wideband spread spectrum based on low speed, low cost ADC (2 Msamples/sec) to subsample Bluetooth spectrum. Further, it leverages a suite of novel signal processing algorithms to demodulate Bluetooth signal in realtime. We implement BlueFunnel prototype based on USRP2 devices. Specifically, we employ two USRR2 devices, each is equipped with SBX daughterboard, to build a customized software radio platform. The customized SDR platform is interfaced to the controller, which implements the digital signal processing algorithms on a personal laptop. We evaluate the system performance based on packet capture rates in a variety of interference conditions, mainly introduce by the 802.11-based WLANs. BlueFunnel maintains good levels of packet capture rates in all settings. Further, we introduce two scenarios of attacks against Bluetooth, where BlueFunnel successfully reveals sensitive information about the target link."--Pages ii-iii.
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Title: Exploiting node mobility for energy optimization in wireless sensor networks
Creator: El-Moukaddem, Fatme Mohammad
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Wireless Sensor Networks (WSNs) have become increasingly available for data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous low-cost robotic units (e.g....
Show moreWireless Sensor Networks (WSNs) have become increasingly available for data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous low-cost robotic units (e.g. Robomote and Khepera) has made it possible to construct sensor networks consisting of mobile sensor nodes. It has been shown that the controlled mobility offered by mobile sensors can be exploited to improve the energy efficiency of a network.In this thesis, we propose schemes that use mobile sensor nodes to reduce the energy consumption of data-intensive WSNs. Our approaches differ from previous work in two main aspects. First, our approaches do not require complex motion planning of mobile nodes, and hence can be implemented on a number of low-cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless communications into a holistic optimization framework.We consider three problems arising from the limited energy in the sensor nodes. In the first problem, the network consists of mostly static nodes and contains only a few mobile nodes. In the second and third problems, we assume essentially that all nodes in the WSN are mobile. We first study a new problem called max-data mobile relay configuration (MMRC) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies and applications. Second, we consider the problem of minimizing the total energy consumption of a network. We design an iterative algorithm that improves a given configuration by relocating nodes to new positions. We show that this algorithm converges to the optimal configuration for the given transmission routes. Moreover, we propose an efficient distributed implementation that does not require explicit synchronization. Finally, we consider the problem of maximizing the lifetime of the network. We propose an approach that exploits the mobility of the nodes to balance the energy consumption throughout the network. We develop efficient algorithms for single and multiple round approaches. For all three problems, we evaluate the efficiency of our algorithms through simulations. Our simulation results based on realistic energy models obtained from existing mobile and static sensor platforms show that our approaches significantly improve the network's performance and outperform existing approaches.
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Title: Human activity monitoring by smart devices
Creator: Bi, Chongguang
Date: 2020
Collection: Electronic Theses & Dissertations
Description: The topic of the Internet-of-Things (IoT) has been discussed and studied extensively since 2010. It provides various solutions for enhancing the user's experience, monitoring the user's behaviors, and improving the lifestyle. With careful design, these systems can be built with off-the-shelf smartphones and wearables. The detected result can be used as feedback for the user to understand his/her behavior, improve the lifestyle, or avoid the danger. Furthermore, the result also provides a...
Show moreThe topic of the Internet-of-Things (IoT) has been discussed and studied extensively since 2010. It provides various solutions for enhancing the user's experience, monitoring the user's behaviors, and improving the lifestyle. With careful design, these systems can be built with off-the-shelf smartphones and wearables. The detected result can be used as feedback for the user to understand his/her behavior, improve the lifestyle, or avoid the danger. Furthermore, the result also provides a valuable data source for the studies in psychology and sociology.However, designing an IoT system to monitor human activities is challenging due to multiple factors. Some systems require high computing capability or a long time of data collection; some systems must detect some specific behaviors as quickly as possible in real-time; some systems suffer constant and irregular noise. In order to address these challenges, the designer must carefully consider the use case of the IoT system and select proper machine learning algorithms. This dissertation shows three designs of the IoT systems for the improvement of family mealtime experience and driving safety. The procedure for each design is introduced in detail, including the architecture of the system, the selection of features, and the evaluation of algorithms. From the case studies in this dissertation, several special aspects of monitoring human activities are discovered. Since human activity is strongly related to the time-series and may change along time, the algorithm should be sensitive to context, be adaptive to dynamic conditions, process readable features, and benefit directly from prior knowledge. This discovery will serve as a guide about how to analyze and solve a problem with the IoT systems in the future.
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Title: A study of Bluetooth Frequency Hopping sequence : modeling and a practical attack
Creator: Albazrqaoe, Wahhab
Date: 2011
Collection: Electronic Theses & Dissertations
Description: The Bluetooth is a wireless interface that enables electronic devices to establish short-range, ad-hoc wireless connections. This kind of short-range wireless networking is known as Wireless Personal Area Networks (WPAN). Because of its attractive features of small size, low cost, and low power, Bluetooth gains a world wide usage. It is embedded in many portable computing devices and considered as a good replacement for local wire connections. Since wireless data is inherently exposed to...
Show moreThe Bluetooth is a wireless interface that enables electronic devices to establish short-range, ad-hoc wireless connections. This kind of short-range wireless networking is known as Wireless Personal Area Networks (WPAN). Because of its attractive features of small size, low cost, and low power, Bluetooth gains a world wide usage. It is embedded in many portable computing devices and considered as a good replacement for local wire connections. Since wireless data is inherently exposed to eavesdropping, the security and confidentiality is a central issue for wireless standard as well as Bluetooth. To maintain security and confidentiality of wireless packets, the Bluetooth system mainly relies on the Frequency Hopping mechanism to equivocate an adversary. By this technique, a wireless channel is accessed for transmitting a packet. For each wireless packet, a single channel is selected in a pseudo random way. This kind of randomness in channel selection makes it difficult for an eavesdropped to predict the next channel to be accessed. Hence, capturing Bluetooth wireless packets is a challenge. In this work, we investigate the Frequency Hopping sequence and specifically the hop selection kernel. We analyze the operation of the kernel hardware by partitioning it into three parts. Based on this modeling, we propose an attacking method for the hop selection kernel. The proposed method shows how to expose the clock value hidden in the kernel. This helps to predict Bluetooth hopping sequence and, hence, capturing Bluetooth wireless packet is possible.
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Title: Empowering Internet of Things with the Emerging Wireless Infrastructures and Technologies
Creator: Yang, Deliang
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Wireless technologies have been evolved rapidly, whose infrastructures are built and delivered speedily. The emerging wireless technologies offer new solutions for data communication, monitoring, sensing, and edge computing, etc. The fast growth of wireless networks generates not only opportunities for new applications, but also issues in high energy consumption, unexpected latency, and potential privacy breach. In this dissertation, we propose two novel cyber-physical systems to demonstrate...
Show moreWireless technologies have been evolved rapidly, whose infrastructures are built and delivered speedily. The emerging wireless technologies offer new solutions for data communication, monitoring, sensing, and edge computing, etc. The fast growth of wireless networks generates not only opportunities for new applications, but also issues in high energy consumption, unexpected latency, and potential privacy breach. In this dissertation, we propose two novel cyber-physical systems to demonstrate the possibility of empowering new IoT services and applications by leveraging the emerging wireless charging infrastructures and benchmarking the energy performance of end nodes in low-power wireless networks, respectively. First, we present QID, the first system that identifies a Qi-compliant device during wireless charging in real-time using wireless charging fingerprints. QID employs a 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. With the novel mobile coil design and a set of novel fingerprints from oscillator and controller patterns, QID achieves high device recognition accuracy by using ensembled Machine Learning algorithms. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy. Second, we develop a novel benchmarking ecosystem, called \textit{NB-Scope}, to study the energy performance of the Narrowband Internet of Things (NB-IoT) network. NB-Scope adopts a hierarchical design, resolving the heterogeneity in network operators, node module vendors, and location profiles, to allow for the fusion of fine-grained diagnostic traces and current measurement. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 3 regions for three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. By extensive data analysis, we identify several key factors, including diverse network coverage levels, long-tail power profile, and excessive control message repetitions, that lead to high variance in the energy performance. Finally, we explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets. Our study on the NB-IoT network provides important insights into the energy consumption of low-power wide-area networks and empowers the IoT applications.
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Title: High-precision and Personalized Wearable Sensing Systems for Healthcare Applications
Creator: Tu, Linlin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The cyber-physical system (CPS) has been discussed and studied extensively since 2010. It provides various solutions for monitoring the user's physical and psychological health states, enhancing the user's experience, and improving the lifestyle. A variety of mobile internet devices with built-in sensors, such as accelerators, cameras, PPG sensors, pressure sensors, and the microphone, can be leveraged to build mobile cyber-physical applications that collected sensing data from the real world...
Show moreThe cyber-physical system (CPS) has been discussed and studied extensively since 2010. It provides various solutions for monitoring the user's physical and psychological health states, enhancing the user's experience, and improving the lifestyle. A variety of mobile internet devices with built-in sensors, such as accelerators, cameras, PPG sensors, pressure sensors, and the microphone, can be leveraged to build mobile cyber-physical applications that collected sensing data from the real world, had data processed, communicated to the internet services and transformed into behavioral and physiological models. The detected results can be used as feedback to help the user understand his/her behavior, improve the lifestyle, or avoid danger. They can also be delivered to therapists to facilitate their diagnose. Designing CPS for health monitoring is challenging due to multiple factors. First of all, high estimation accuracy is necessary for health monitoring. However, some systems suffer irregular noise. For example, PPG sensors for cardiac health state monitoring are extremely vulnerable to motion noise. Second, to include human in the loop, health monitoring systems are required to be user-friendly. However, some systems involve cumbersome equipment for a long time of data collection, which is not feasible for daily monitoring. Most importantly, large-scale high-level health-related monitoring systems, such as the systems for human activity recognition, require high accuracy and communication efficiency. However, with users' raw data uploading to the server, centralized learning fails to protect users' private information and is communication-inefficient. The research introduced in this dissertation addressed the above three significant challenges in developing health-related monitoring systems. We build a lightweight system for accurate heart rate measurement during exercise, design a smart in-home breathing training system with bio-Feedback via virtual reality (VR) game, and propose federated learning via dynamic layer sharing for human activity recognition.
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