This research presents both theoretical foundation and numerical simulation work for design and analysis of a multi-agent dynamic system on the collective formation behavior patterns of grouped agents. A mass model with tunable control parameters is proposed. This model can realistically represent the aggregation pattern and the formation shape of multiple agents. Stability analysis is also provided to prove the stability of the second-order dynamic system. Several simulations will also be given according to the proposed model to show the aggregation patterns. The research on self-organizing characteristics of collective agent behaviors has a wide range of applications in nature and engineering. The formation such as a flock of birds, a school of fish, or a swarm of locusts, is the emergence of ordered state in which the moving agents can organize as formation. Design and control of the self-organizing dynamic system has implications on wireless general design of mobile sensor networks, sensor network data fusion, attitude alignment of satellite clusters and congestion control of communication networks. / Electrical and Computer Engineering

Energy efficiency is a critical feature of wireless sensor networks (WSNs), because sensor nodes run on batteries that are generally difficult to recharge once deployed. For target tracking---one of the most important WSN application types---energy efficiency needs to be considered in various forms and shapes, such as idle listening, trajectory estimation, and data propagation. In this dissertation, we study three correlated problems on energy efficient target tracking in WSNs: sleep scheduling, particle filtering, and constrained flooding. We develop a Target Prediction and Sleep Scheduling protocol (TPSS) to improve energy efficiency for idle listening. We start with designing a target prediction method based on both kinematics and probability. Based on target prediction and proactive wake-up, TPSS precisely selects the nodes to awaken and reduces their active time, so as to enhance energy efficiency with limited tracking performance loss. In addition, we expand Sleep Scheduling to Multiple Target Tracking (SSMTT), and further reduce the energy consumption by leveraging the redundant alarm messages of interfering targets. Our simulation-based experimental studies show that compared to existing protocols such as Circle scheme and MCTA, TPSS and SSMTT introduce an improvement of 25% ~ 45% on energy efficiency, at the expense of only 5% ~ 15% increase on the detection delay. Particle Filtering is one of the most widely used Bayesian estimation methods, when target tracking is considered as a dynamic state estimation problem for trajectory estimation. However, the significant computational and communication complexity prohibits its application in WSNs. We design two particle filters (PFs)---Vector space based Particle Filter (VPF) and Completely Distributed Particle Filter (CDPF)---to improve energy efficiency of PFs by reducing the number of particles and the communication cost. Our experimental evaluations show that even though VPF incurs 34% more estimation error than RPF, and CDPF incurs a similar estimation error to SDPF, they significantly improve the energy efficiency by as much as 68% and 90% respectively. For data propagation, we present a Constrained Flooding protocol (CFlood) to enhance energy efficiency by increasing the deadline satisfaction ratio per unit energy consumption of time-sensitive packets. CFlood improves real-time performance by flooding, but effectively constrains energy consumption by controlling the scale of flooding---i.e., flooding only when necessary. If unicasting meets the distributed sub-deadline at a hop, CFlood aborts further flooding even after flooding has occurred in the current hop. Our simulation-based experimental studies show that CFlood achieves higher deadline satisfaction ratio per unit energy consumption by as much as 197%, 346%, and 20% than existing multipath forwarding protocols, namely, Mint Routing, MCMP and DFP respectively, especially in sparsely deployed or unreliable sensor network environments. To verify the performance and efficiency of the dissertation's solutions, we developed a prototype implementation based on TelosB motes and TinyOS version 2.1.1. In the field experiments, we compared TPSS, VPF, CDPF, and CFlood algorithms/protocols to their respective competing efforts. Our implementation measurements not only verified the rationality and feasibility of the proposed solutions for target tracking in WSNs, but also strengthened the observations on their efficiency from the simulation. / Ph. D.

Structural Health Monitoring (SHM) is the science and technology of monitoring and can assess the condition of aerospace, civil, and mechanical infrastructures using a sensing system integrated into the structure. SHM is capable of detecting, locating, and quantifying various types of damage such as cracks, holes, corrosion, delamination, and loose joints, and can be applied to various kinds of infrastructures such as buildings, railroads, windmills, bridges, and aircraft. A major technical challenge for existing SHM systems is high power consumption, which severely limits the range of its applications. In this thesis, we investigated adoption of acoustic emission detection to reduce power dissipation of SHM systems employing the impedance and the Lamb wave methods. An acoustic emission sensor of the proposed system continuously monitors acoustic events, while the SHM system is in sleep mode. The SHM system is evoked to perform the SHM operation only when there is an acoustic event detected by the acoustic emission sensor. The proposed system avoids unnecessary operation of SHM operations, which saves power, and the system is effective for certain applications such as spacecraft and wind turbine blades. We developed prototype systems using a Texas Instruments TMS320F2812 DSP evaluation board for the Lamb wave method and an MSP430 evaluation board for the impedance method. / Master of Science

Power consumption and battery life are of critical importance for medical implant devices. For this reason, devices for Wireless Body Area Network (WBAN) applications must consume very little power. To save power, it is desirable to turn off or put to sleep a device when not in use. However, a transceiver, which is the most power hungry block of a wireless sensor node, needs to listen for the incoming signal continuously. An alternative scheme, is to listen for the incoming signal at a predetermined internal, which saves power at the cost of increased latency. Another and more sophisticated scheme is to provide a wake-up receiver, which listens for the incoming signal continuously, and upon detection of an incoming signal, it wakes the primary transceiver up. A wake-up receiver is typically simple and dissipates little power to make the scheme useful. This thesis proposes a low-power wake-up receiver, which listens for a wake-up signal, identifies the target node, and wakes up the primary receiver only when that specific node is called upon. When a wake up signal is transmitted to all of the nodes on a network, our wake-up receiver allows all the nodes on a network except the targeted node to remain asleep to save power. Several wake-up receiver topologies have been proposed. This work uses a passive Cockcroft-Walton multiplier circuit as an RF envelope detector followed by a simple detector circuit. A novel serial code detector is then used to decode the pulse width modulated input signal to wake-up the designated node. A passive RF front end and simple decoding circuit reduce power consumption substantially at the cost of low sensitivity. The sensitivity of the wake-up receiver can be improved though the addition of an RF amplifier, but at the cost of increased power consumption. / Master of Science

As resident space object populations grow, and satellite propulsion capabilities improve, it will become increasingly challenging for space-reliant nations to maintain space situational awareness using current human-in-the-loop methods. This dissertation develops several real-time adaptive approaches to autonomous sensor network management for tracking multiple maneuvering and non-maneuvering satellites with a diversely populated Space Object Surveillance and Identification network. The proposed methods integrate suboptimal Partially Observed Markov Decision Processes (POMDPs) with covariance inflation or multiple model adaptive estimation techniques to task sensors and maintain viable orbit estimates for all targets. The POMDPs developed in this dissertation use information-based and system-based metrics to determine the rewards and costs associated with tasking a specific sensor to track a particular satellite. Like in real-world situations, the population of target satellites vastly outnumbers the available set of sensors. Robust and adaptable tasking algorithms are needed in this scenario to determine how and when sensors should be tasked. The strategies developed in this dissertation successfully track 207 non-maneuvering and maneuvering spacecraft using only 24 ground and space-based sensors. The results show that multiple model adaptive estimation coupled with a multi-metric, suboptimal POMDP can effectively and efficiently task a diverse network of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for autonomous and adaptable sensor network command and control for real-world space situational awareness. / Ph. D. / As the number of spacecraft in orbit increase, and satellite propulsion capabilities improve, it will become increasingly difficult for space-reliant nations to keep track of every object orbiting earth using human-in-the-loop methods. Already, the population of target satellites vastly outnumbers the available set of sensors. At any given time, a given network of sensors cannot observe every satellite in orbit, and must manage the available sensors effectively to keep track of every object of interest. The ability to maintain actionable knowledge of every orbiting object of interest is known as space situational awareness. Conventional tracking processes have generally not changed for decades, and were designed when there were far fewer satellites in orbit with little or no ability to maneuver. These methods involve large numbers of operators and engineers who schedule a network of sensors under the assumption that the satellites will not unexpectedly change their orbits for long periods of time. In the near future, traditional space surveillance approaches will become insufficient at maintaining space situational awareness, particularly if more satellites conduct unanticipated maneuvers. This dissertation develops several real-time approaches for controlling a diverse network of ground and space-based sensors that remove the need for human intervention. These fully computer-based command and control processes adapt to dynamic situations and automatically task sensors to rapidly track multiple maneuvering and non-maneuvering satellites. The decision processes used to determine which sensors should be tasked to observe a particular spacecraft compare the amount of information that can be collected in a single observation and the workload a sensor must execute to collect the observation. The command and control strategies developed in this dissertation successfully track 207 non-maneuvering and maneuvering spacecraft using only 24 ground and space-based sensors. The results show that adaptive, fully autonomous sensor network control processes can effectively and efficiently task a diverse set of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for adaptive, computer-based sensor network command and control for real-world space situational awareness. This research was supported by the Virginia Tech New Horizons Graduate Scholar Program, the Ted and Karyn Hume Center for National Security and Technology, the DARPA Hallmark program, and the U.S. Joint Warfare Analysis Center.

Yes / Underwater communication applications extensively use localization services for object identification. Because of their significant impact on ocean exploration and monitoring, underwater wireless sensor networks (UWSN) are becoming increasingly popular, and acoustic communications have largely overtaken radio frequency (RF) broadcasts as the dominant means of communication. The two localization methods that are most frequently employed are those that estimate the angle of arrival (AOA) and the time difference of arrival (TDoA). The military and civilian sectors rely heavily on UWSN for object identification in the underwater environment. As a result, there is a need in UWSN for an accurate localization technique that accounts for dynamic nature of the underwater environment. Time and position data are the two key parameters to accurately define the position of an object. Moreover, due to climate change there is now a need to constrain energy consumption by UWSN to limit carbon emission to meet net-zero target by 2050. To meet these challenges, we have developed an efficient localization algorithm for determining an object position based on the angle and distance of arrival of beacon signals. We have considered the factors like sensor nodes not being in time sync with each other and the fact that the speed of sound varies in water. Our simulation results show that the proposed approach can achieve great localization accuracy while accounting for temporal synchronization inaccuracies. When compared to existing localization approaches, the mean estimation error (MEE) and energy consumption figures, the proposed approach outperforms them. The MEEs is shown to vary between 84.2154m and 93.8275m for four trials, 61.2256m and 92.7956m for eight trials, and 42.6584m and 119.5228m for twelve trials. Comparatively, the distance-based measurements show higher accuracy than the angle-based measurements.

Locating a radiating source based on range or range measurements obtained from a network of passive sensors has been a subject of research over the past two decades due to the problem’s importance in applications in wireless communications, surveillance, navigation, geosciences, and several other fields. In this thesis, we develop new solution methods for the problem of localizing a single radiating source based on range and range-difference measurements. Iterative re-weighting algorithms are developed for both range-based and range-difference-based least squares localization. Then we propose a penalty convex-concave procedure for finding an approximate solution to nonlinear least squares problems that are related to the range measurements. Finally, the sequential convex relaxation procedures are proposed to obtain the nonlinear least squares estimate of source coordinates. Localization in wireless sensor network, where the RF signals are used to derive the ranging measurements, is the primary application area of this work. However, the solution methods proposed are general and could be applied to range and range-difference measurements derived from other types of signals. / Graduate / 0544 / ismailds@uvic.ca

The purpose of this thesis was to design and implement a prototype of binary topology sensor network (as proposed in patent CZ 306855 B6). Important part was evaluation of this type of network. A survey of commonly used sensors and technologies used in intelligent buildings and sensor networks was created. The gathered information was used for the evaluation. A network for analog signal transmission was designed and implemented (including all the elements) based on the patent proposal. A number of configurations was proposed to test the network features. Measurements were performed using all the proposed configurations. Two devices were used for the measurement: STM32 Nucleo F303K8, MyRIO (by National Instruments). Looking at the measured data, we can tell that the consistency is good enough to declare this network functional as a concept. However, we cannot recommend this type of network for high precision applications at the time, due to some accuracy and consistency issues. We have also detected relatively significant noise in the data. That could be caused by many external influences, partly by faulty design of the components.

Le développement des réseaux de capteurs sans fil fait que chaque utilisateur ou organisation est déjà connecté à un nombre important de nœuds. Ces nœuds génèrent une quantité importante de données, rendant la gestion de ces données non évident. De plus, ces données peuvent contenir des informations concernant la vie privée. Les travaux de la thèse attaquent ces problématiques. Premièrement, nous avons conçu un middleware qui communique avec les capteurs physiques pour collecter, stocker, traduire, indexer, analyser et générer des alertes sur les données des capteurs. Ce middleware est basé sur la notion de composants et de composites. Chaque nœud physique communique avec un composite du middleware via une interface RESTFul. Ce middleware a été testé et utilisé dans le cadre du projet Européen Mobesens dans le but de gérer les données d'un réseau de capteurs pour la surveillance de la qualité de l'eau. Deuxièmement, nous avons conçu un protocole hybride d'authentification et d'établissement de clés de paires et de groupes. Considérant qu'il existe une différence de performance entre les noeuds capteur, la passerelle et le middleware, nous avons utilisé l'authentification basé sur la cryptographie basée sur les identités entre la passerelle et le serveur de stockage et une cryptographie symétrique entre les capteurs et les deux autres parties. Ensuite, le middleware a été généralisé dans la troisième partie de la thèse pour que chaque organisation ou individu puisse avoir son propre espace pour gérer les données de ses capteurs en utilisant le cloud computing. Ensuite, nous avons portail social sécurisé pour le partage des données des réseaux de capteurs / Nowadays, each user or organization is already connected to a large number of sensor nodes which generate a substantial amount of data, making their management not an obvious issue. In addition, these data can be confidential. For these reasons, developing a secure system managing the data from heterogeneous sensor nodes is a real need. In the first part, we developed a composite-based middleware for wireless sensor networks to communicate with the physical sensors for storing, processing, indexing, analyzing and generating alerts on those sensors data. Each composite is connected to a physical node or used to aggregate data from different composites. Each physical node communicating with the middleware is setup as a composite. The middleware has been used in the context of the European project Mobesens in order to manage data from a sensor network for monitoring water quality. In the second part of the thesis, we proposed a new hybrid authentication and key establishment scheme between senor nodes (SN), gateways (MN) and the middleware (SS). It is based on two protocols. The first protocol intent is the mutual authentication between SS and MN, on providing an asymmetric pair of keys for MN, and on establishing a pairwise key between them. The second protocol aims at authenticating them, and establishing a group key and pairwise keys between SN and the two others. The middleware has been generalized in the third part in order to provide a private space for multi-organization or -user to manage his sensors data using cloud computing. Next, we expanded the composite with gadgets to share securely sensor data in order to provide a secure social sensor network

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