Wireless sensor networks (WSNs) comprise of highly power constrained nodes that observe a hidden natural field and reconstruct it at a distant data fusion center. Algorithmic strategies for extending the lifetime of s...
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Wireless sensor networks (WSNs) comprise of highly power constrained nodes that observe a hidden natural field and reconstruct it at a distant data fusion center. Algorithmic strategies for extending the lifetime of such networks invariably require a knowledge of the statistical model of the underlying field. Since centralized model identification is communication intensive and eats into any potential power savings, we present a stochastic recursive identification algorithm which can be implemented in a fully distributed and scalable manner within the network. We demonstrate that it consumes modest resources relative to centralized estimation, and is stable, unbiased, and asymptotically efficient.
We consider a wireless sensor network (WSN) that monitors a physical field and communicates pertinent data to a distant fusion center (FC). We study the case of a binary valued hidden natural field observed in a signi...
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We consider a wireless sensor network (WSN) that monitors a physical field and communicates pertinent data to a distant fusion center (FC). We study the case of a binary valued hidden natural field observed in a significant amount of Gaussian clutter, which is relevant to applications like detection of plumes or oil slicks. The considerable spatio-temporal dependencies found in natural fields can be exploited to improve the reliability of the detection/estimation of hidden phenomena. While this problem has been previously treated using kernel-regression techniques, we formulate it as a task of delay-free filtering on a process observed by the sensors. We propose a distributed scalable implementation of the filter within the network. This is achieved by i) exploiting the localized spatio-temporal dependencies to define a hidden Markov model (HMM) in terms of an exponential family with O(N) parameters, where N is the size of the WSN, ii) using a reduced-state approximation of the propagated probability mass function, and iii) making a tractable approximation of model marginals by using iterated decoding algorithms like the Gibbs sampler (GS), mean field decoding (MFD), iterated conditional modes (ICM), and broadcast belief propagation (BBP). We compare the marginalization algorithms in terms of their information geometry, performance, complexity and communication load. Finally, we analyze the energy efficiency of the proposed distributed filter relative to brute force data fusion. It is demonstrated that when the FC is sufficiently far away from the sensor array, distributed filtering is Significantly more energyefficient and can increase the lifetime of the WSN by one to two orders of magnitude.
This work considers the problem of broadcast routing in a wireless ad hoc network from the viewpoint of energy efficiency. Each node in a wireless ad hoc network runs on a local energy source which has a limited energ...
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This work considers the problem of broadcast routing in a wireless ad hoc network from the viewpoint of energy efficiency. Each node in a wireless ad hoc network runs on a local energy source which has a limited energy life span. Thus, energy conservation is a critical issue in such networks. In this paper, we address the problem of finding a broadcast tree for a given network that maximizes the minimum residual battery capacity available among all nodes in the network. We call this problem the Maximum Residual Battery Capacity Broadcast Routing Problem (RBBP). We propose a new algorithm for RBBP and prove that RBBP is optimally solvable in polynomial time using the proposed algorithm. In addition, we show that the problem of finding a maximum residual battery capacity broadcast tree with minimum total energy consumption is NP-complete and we propose a new heuristic algorithm for this problem. (c) 2005 Elsevier B.V. All rights reserved.
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