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Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the network lifetime. In this dissertation, we consider a network of imaging sensors. We address the problem of energy-efficient communication of the resulting measurements. First, we develop a heuristic-based method that exploits the redundancy in the measurements of imaging sensors. The algorithm attempts to maximize the lifetime of the network without utilizing inter-sensor communication. Gains in network lifetime up to 114% are obtained when using the suggested algorithm with lossless compression. Our results also demonstrate that when lossy compression is employed, much larger gains are achieved. For example, when a normalized Root-Mean-Squared- Error of 0.78% can be tolerated in the received measurements, the network lifetime increases by a factor of 2.8, as compared to the lossless case. Second, we develop a novel theory for maximizing the lifetime of unicast multihop wireless sensor networks. An optimal centralized solution is presented in the form of an iterative algorithm. The algorithm attempts to find a Pareto Optimal solution. In the first iteration, the minimum lifetime of the network is maximized. If the solution is not Pareto Optimal a second iteration is performed which maximizes the second minimum lifetime subject to the minimum lifetime being maximum. At the nth iteration, the algorithm maximizes the nth minimum lifetime subject to the (n−1)th minimum lifetime being maximum, subject to the (n−2)th minimum lifetime being maximum, etc. The algorithm can be stopped at any iteration n. Third, we present a novel algorithm for the purpose of exploiting the inherent inter- and intra-sensor correlation in a network of imaging sensors while utilizing inter-sensor communication. This algorithm combines a collaborative compression method in conjunction with our cooperative multi-hop routing strategy in order to maximize the lifetime of the network. This CMT algorithm is demonstrated to achieve average gain in lifetime as high as 3.2 over previous methods. Finally, we discuss practical implementation considerations of our CMT algorithm. We first present some experimental results that illustrate the practicality of our method. Next, we develop a realistic optical model that permits us to consider a more heterogeneous network of cameras by allowing for varying resolution, intrinsic and extrinsic parameters, point-spread function and detector size. We show that our previous CMT algorithm can be extended to successfully operate in such a diverse imaging model. We propose new object-domain quality metrics and show that our proposed method is able to balance lifetime and fidelity according to expectations.

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In wireless networking, receivers are typically assumed to be utilizing single-user decoding. Still, for more than twenty years we know that we can take advantage of interference by multi-user decoding. The Interference Cancellation (IC) technique has, of late, gained interest in the wireless networking context. Previous works [3] have shown considerable potential gains by leveraging optimal collaborative rate control to enable IC, focusing on the low Signal-to-Noise Ratio (SNR) regime. Here, we present centralized and distributed rate control algorithms, enabling IC, to increase system throughput. We consider a system where the receivers can apply multi-user decoding to perform IC and the rates are provided by a step-wise function of the Signal to Interference-and-Noise Ratio (SINR), in realistic conditions. We conduct a thorough simulation study comparing the proposed algorithms using two IC techniques, and deliver results that indicate significant system throughput gains.

This thesis proposes a distributed observer design for a class of nonlinear systems that arise in the application of model reduction techniques. Distributed observer design techniques have been proposed in the literature to address estimation problems over sensor networks. In large complex sensor networks, an efficient technique that minimizes the extent of the required communication is highly desirable. This is especially true when sensors have problems caused by physical limitations that result in incorrect information at the local level affecting the estimation of states globally. To address this problem, scalable algorithms for a suitable distributed observer have been developed. Most algorithms are focussed on large linear dynamical systems and they are not directly generalizable to nonlinear systems. In this thesis, scalable algorithms for distributed observers are proposed for a class of large scale observable nonlinear system. Distributed systems models multi-agent systems in which each agents attempts to accomplish local tasks. In order to achieve global objectives, there should be agreement regarding some commonly known variables that depend on the state of all agents. These variables are called consensus states. Once identified, such consensus states can be exploited in the development of distributed consensus algorithms. Consensus algorithms are used to develop information exchange protocols between agents such that global objectives are met through local action. In this thesis, a higher order observer is applied in the distributed sensor network system to design a distributed observer for a class nonlinear systems. Fusion of measurement and covariance information is applied to the higher order filter as the first method. The consensus filter is embedded in the local nonlinear observer for fusion of data. The second method is based on the communication of state estimates between neighbouring sensors rather than fusion of data measurement and covariance. The second method is found to reduce disagreement of the states estimation between each sensor. The performance of these new algorithms is demonstrated by simulation, and the second method is effectively applied over the first method. / Thesis (Master, Chemical Engineering) -- Queen's University, 2012-12-12 11:22:49.113

Most of the service discovery protocols available on the Internet are built upon its logical structure. This phenomenon can be observed frequently from the way in which they behave. For instance, Jini and SLP service providers announce their presence by multicasting service advertisements, an approach that is neither intended to scale nor capable of scaling to the size of the Internet. With mobile and wireless devices becoming increasingly popular, there appears to be a need for performing service discovery in a wide-area context, as there is very little direct correlation between the Internet topology and geographic locations. Even for desktop computers, such a need can arise from time to time. This problem suggests the necessity for an architecture that allows users to locate resources on the Internet using geographic criteria. This thesis presents an architecture that can be deployed with minimal effort in the existing network infrastructure. The geographic information can be shared among multiple applications in a fashion similar to the way DNS is shared throughout the Internet. The design and implementation of the architecture are discussed in detail, and three case studies are used to illustrate how the architecture can be employed by various applications to satisfy dramatically different needs of end-users.

This thesis was motivated by a case study involving the formalisation of arguments that simplify the verification of tree-oriented multicast protocols. As well as covering the case study itself, it discusses our solution to problems we encountered concerning expressivity and automation. The expressivity problems related to the need for theory interpretation. We found the existing Locale and axiomatic type class mechanisms provided by the Isabelle theorem prover we were using to be inadequate. This led us to develop a new prototype implementation of theory interpretation. To support this implementation, we developed a novel system of proof terms for the HOL logic that we also describe in this thesis. We found existing automation to perform poorly, which led us to experiment with additional kinds of automation. We describe our approach, focusing on features that make automation suitable for interactive use. Our presentation of the case study starts with our formalisation of an abstract theory of distributed systems, covering state transition systems, forward and backward simulation relations, and related properties of LTL (linear temporal logic). We then summarise proofs of simulation relations holding for particular abstract multicast protocols. We discuss the mechanisation styles we experimented with in the case study. We also discuss the methodology behind our proofs. We cover aspects such as how to discover and construct proofs, and how to explore the space of proofs, how to make good definitions and lemmas, how to increase modularity, reuse, stability and malleability of proofs, and reduce maintenance of proofs, and the gap between intuitively understood proofs and their formalisation.

In this research, the application of intelligent mobile agents to the management of distributed network environments is investigated. Intelligent mobile agents are programs which can move about network systems in a deterministic manner in carrying their execution state. These agents can be considered an application of distributed artificial intelligence where the (usually small) agent code is moved to the data and executed locally. The mobile agent paradigm offers potential advantages over many conventional mechanisms which move (often large) data to the code, thereby wasting available network bandwidth. The performance of agents in network routing and knowledge acquisition has been investigated and simulated. A working mobile agent system has also been designed and implemented in JDK 1.2.