The Wireless Sensor Networks (WSNs) have emerged as a new paradigm for collecting and processing data from physical environments, such as wild life sanctuaries, large warehouses, and battlefields. Users can access sensor data by issuing queries over the network, e.g., to find what are the 10 highest temperature values in the network. Typically, a WSN operates by constructing a logical topology, such as a spanning tree, built on top of the physical topology of the network. The constructed logical topology is then used to disseminate queries in the network, and also to process and return the results of such queries back to the user. A major challenge in this context is prolonging the network's lifetime that mainly depends on the energy cost of data communication via wireless radios, which is known to be very expensive as compared to the cost of data processing within the network. In this research, we investigate some of the core problems that deal with the different aspects of in-network query processing in WSNs. In that context, we propose an efficient filtering based algorithm for the top-k query processing in WSNs. Through a systematic study of the top-k query processing in WSNs we propose several solutions in this thesis, which are applicable not only to the top-k queries, but also to in-network query processing problems in general. Specifically, we consider broadcasting and convergecasting, which are two basic operations that are required by many in-network query processing solutions. Scheduling broadcasting and convergecasting is another problem that is important for energy efficiency in WSNs. Failure of communication links, which are common in WSNs, is yet another important issue that needs to be addressed. In this research, we take a holistic approach to deal with the above problems while processing the top-k queries in WSNs. To this end, the thesis makes several contributions. In particular, our proposed solutions include new logical topologies, scheduling algorithms, and an overall sophisticated communication framework, which allows to process the top-k queries efficiently and with increased reliability. Extensive simulation studies reveal that our solutions are not only energy efficient, saving up to 50% of the energy cost as compared to the current state-of-the-art solutions, but they are also robust to link failures.

This dissertation studies the feature-based image comparison method and its application in Wireless Visual Sensor Networks. Wireless Visual Sensor Networks (WVSNs), formed by a large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Although each single sensor has very limited capability in sensing, processing and transmission, by working together they can achieve various high level tasks. Sensor collaboration is essential to WVSNs and normally performed among sensors having similar measurements, which are called neighbor sensors. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. Besides, the energy resource on the WVSNs is also very tight, with wireless communication and complicated computation consuming most energy in WVSNs. Therefore the feature-based image comparison method has been proposed, which directly compares the captured image from each visual sensor in an economical way in terms of both the computational cost and the transmission overhead. The feature-based image comparison method compares different images and aims to find similar image pairs using a set of local features from each image. The image feature is a numerical representation of the raw image and can be more compact in terms of the data volume than the raw image. The feature-based image comparison contains three steps: feature detection, descriptor calculation and feature comparison. For the step of feature detection, the dissertation proposes two computationally efficient corner detectors. The first detector is based on the Discrete Wavelet Transform that provides multi-scale corner point detection and the scale selection is achieved efficiently through a Gaussian convolution approach. The second detector is based on a linear unmixing model, which treats a corner point as the intersection of two or three “line” bases in a 3 by 3 region. The line bases are extracted through a constrained Nonnegative Matrix Factorization (NMF) approach and the corner detection is accomplished through counting the number of contributing bases in the linear mixture. For the step of descriptor calculation, the dissertation proposes an effective dimensionality reduction algorithm for the high dimensional Scale Invariant Feature Transform (SIFT) descriptors. A set of 40 SIFT descriptor bases are extracted through constrained NMF from a large training set and all SIFT descriptors are then projected onto the space spanned by these bases, achieving dimensionality reduction. The efficiency of the proposed corner detectors have been proven through theoretical analysis. In addition, the effectiveness of the proposed corner detectors and the dimensionality reduction approach has been validated through extensive comparison with several state-of-the-art feature detector/descriptor combinations.

The objective of this thesis is the development of cooperative localization and tracking algorithms using nonparametric message passing techniques. In contrast to the most well-known techniques, the goal is to estimate the posterior probability density function (PDF) of the position of each sensor. This problem can be solved using Bayesian approach, but it is intractable in general case. Nevertheless, the particle-based approximation (via nonparametric representation), and an appropriate factorization of the joint PDFs (using message passing methods), make Bayesian approach acceptable for inference in sensor networks. The well-known method for this problem, nonparametric belief propagation (NBP), can lead to inaccurate beliefs and possible non-convergence in loopy networks. Therefore, we propose four novel algorithms which alleviate these problems: nonparametric generalized belief propagation (NGBP) based on junction tree (NGBP-JT), NGBP based on pseudo-junction tree (NGBP-PJT), NBP based on spanning trees (NBP-ST), and uniformly-reweighted NBP (URW-NBP). We also extend NBP for cooperative localization in mobile networks. In contrast to the previous methods, we use an optional smoothing, provide a novel communication protocol, and increase the efficiency of the sampling techniques. Moreover, we propose novel algorithms for distributed tracking, in which the goal is to track the passive object which cannot locate itself. In particular, we develop distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Finally, the last part of this thesis includes the experimental analysis of some of the proposed algorithms, in which we found that the results based on real measurements are very similar with the results based on theoretical models.

Context represents any knowledge obtained from Wireless Sensor Networks (WSNs) about the object being monitored (such as time and location of the sensed events). Time and location are important constituents of context as the information about the events sensed in WSNs is comprehensive when it includes spatio-temporal knowledge. In this thesis, we first concentrate on the development of a suite of lightweight algorithms on temporal event ordering and time synchronization as well as localization for WSNs. Then, we propose an energy-efficient clustering routing protocol for WSNs that is used for message delivery in the former algorithm. The two problems - temporal event ordering and synchronization - are dealt with together as both are concerned with preserving temporal relationships of events in WSNs. The messages needed for synchronization are piggybacked onto the messages exchanged in underlying algorithms. The synchronization algorithm is tailored to the clustered topology in order to reduce the overhead of keeping WSNs synchronized. The proposed localization algorithm has an objective of lowering the overhead of DV-hop based algorithms by reducing the number of floods in the initial position estimation phase. It also randomizes iterative refinement phase to overcome the synchronicity of DV-hop based algorithms. The position estimates with higher confidences are emphasized to reduce the impact of erroneous estimates on the neighbouring nodes. The proposed clustering routing protocol is used for message delivery in the proposed temporal algorithm. Nearest neighbour nodes are employed for inter-cluster communication. The algorithm provides Quality of Service by forwarding high priority messages via the paths with the least cost. The algorithm is also extended for multiple Sink scenario. The suite of algorithms proposed in this thesis provides the necessary tool for providing spatio-temporal context for context-aware WSNs. The algorithms are lightweight as they aim at satisfying WSN's requirements primarily in terms of energy-efficiency, low latency and fault tolerance. This makes them suitable for emergency response applications and ubiquitous computing.

In this thesis, an investigation of the performance of multipath routing in Wireless Sensor Networks (WSN) is performed. The communication in the network under study is to take place from individual nodes to the sink node. The investigation involved multipath finding methods in WSN. Also, it involves investigating the weight assignment, traffic splitting and route selection methods for the different paths discovered by each node in the WSN. Also, a comparison between Hybrid Routing Protocol, Source Routing Protocol and Diffusion Routing Protocol is performed. A simple traffic routing algorithm for each routing protocol has been developed to conceptualize how the network traffic is routed on a set of active paths. The investigation of the Hybrid, Source and Diffusion Routing Protocol involved using multiple paths simultaneously to transmit messages that belong to the same flow by using a weight assigned to each path and transmit each message as a whole. Finally, the power consumption and the QoS in terms of message delays for a WSN were investigated and compared between different protocols.

Data aggregation is an important technique in wireless sensor networks. The data are gathered together by data fusion routines along the routing path, which is called data-centralized routing. We propose a localized, Delay-bounded and Energy-efficient Data Aggregation framework(DEDA) based on the novel concept of DEsired Progress (DEP). This framework works under request-driven networks with realistic MAC layer protocols. It is based on localized minimal spanning tree (LMST) which is an energy-efficient structure. Besides the energy consideration, delay reliability is also considered by means of the DEP. A node’s DEP reflects its desired progress in LMST which should be largely satisfied. Hence, the LMST edges might be replaced by unit disk graph (UDG) edges which can progress further in LMST. The DEP metric is rooted on realistic degree-based delay model so that DEDA increases the delay reliability to a large extent compared to other hop-based algorithms. We also combine our DEDA framework with area coverage and localized connected dominating set algorithms to achieve two more resilient DEDA implementations: A-DEDA and AC-DEDA. The simulation results confirm that our original DEDA and its two enhanced variants save more energy and attain a higher delay reliability ratio than existing protocols.

Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

Wireless Sensor Networks (WSNs) have enticed a strong attention in the research community due to the broad range of applications and services they support. WSNs are composed of intelligent sensor nodes that have the capabilities to monitor different types of environmental phenomena or critical activities. Sensor nodes operate under stringent requirements of scarce power resources, limited storage capacities, limited processing capabilities, and hostile environmental surroundings. However, conserving sensor nodes’ power resources is the top priority requirement in the design of a WSN as it has a direct impact on its lifetime. The IEEE 802.15.4 standard defines a set of specifications for both the PHY layer and the MAC sub-layer that abide by the distinguished requirements of WSNs. The standard’s MAC protocol employs an intelligent backoff algorithm, called the Binary Exponent Backoff (BEB), that minimizes the drainage of power in these networks. In this thesis we present an in-depth study of the IEEE 802.15.4 MAC protocol to highlight both its strong and weak aspects. We show that we have enticing opportunities to improve the performance of this protocol in the context of WSNs. We propose three new backoff algorithms, namely, the Standby-BEB (SB-BEB), the Adaptive Backoff Algorithm (ABA), and the Priority-Based BEB (PB-BEB), to replace the standard BEB. The main contribution of the thesis is that it develops a new design concept that drives the design of efficient backoff algorithms for the IEEE 802.15.4-based WSNs. The concept dictates that controlling the algorithms parameters probabilistically has a direct impact on enhancing the backoff algorithm’s performance. We provide detailed discrete-time Markov-based models (for AB-BEB and ABA) and extensive simulation studies (for the three algorithms) to prove the superiority of our new algorithms over the standard BEB.

Wireless sensor networks offer the advantages of low cost, flexible measurement of phenomenon in a wide variety of applications, and easy deployment. Since sensor nodes are typically battery powered, energy efficiency is an important objective in designing sensor network algorithms. These algorithms are often application-specific, owing to the need to carefully optimize energy usage, and since deployments usually support a single or very few applications.<p> This thesis concerns applications in which the sensors monitor a continuous scalar field, such as temperature, and addresses the problem of determining the location of a contour line in this scalar field, in response to a query, and communicating this information to a designated sink node. An energy-efficient solution to this problem is proposed and evaluated. This solution includes new contour detection and query propagation algorithms, in-network-processing algorithms, and routing algorithms. Only a small fraction of network nodes may be adjacent to the desired contour line, and the contour detection and query propagation algorithms attempt to minimize processing and communication by the other network nodes. The in-network processing algorithms reduce communication volume through suppression, compression and aggregation techniques. Finally, the routing algorithms attempt to route the contour information to the sink as efficiently as possible, while meshing with the other algorithms. Simulation results show that the proposed algorithms yield significant improvements in data and message volumes compared to baseline models, while maintaining the integrity of the contour representation.

Wireless sensor networks (WSNs) are now widely used in many applications. Connected Dominating Set (CDS) based routing which is one kind of hierarchical methods has received more attention to reduce routing overhead. The concept of k-connected m-dominating sets (kmCDS) is used to provide fault tolerance and routing flexibility. In this thesis, we first consider how to construct a CDS in WSNs. After that, centralized and distributed algorithms are proposed to construct a kmCDS. Moreover, we introduce some basic ideas of how to use CDS in other potential applications such as partial coverage and data dissemination in WSNs.