ISSN : 1796-2021
Volume : 1    Issue : 6    Date : September 2006

Joint Optimization of Local and Fusion Rules in a Decentralized Sensor Network
Nithya Gnanapandithan and Balasubramaniam Natarajane
Page(s): 9-17
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Decentralized sensor networks are collections of individual local sensors that observe a common
phenomenon, quantize their observations, and send this quantized information to a central
processor (fusion center) which then makes a global decision about the phenomenon. Most of the
existing literature in this field consider only the data fusion aspect of this problem, i.e., the statistical
hypothesis testing and optimal combining of the information obtained by the local sensors. In this
paper, we propose a Parallel Genetic Algorithm (PGA) for optimizing the probability of global
detection error performance of a parallel decentralized sensor network. Specifically, we use the PGA
to simultaneously optimize both the fusion rule and the local decision rules. We show that our
approach provides results comparable to those obtained by using a GA and gradient-based
algorithm from previous work by Aldosari and Moura, with reduced complexity. We consider
both the cases of identical (homogeneous) and non-identical (heterogeneous) sensors and
demonstrate that our algorithm converges to the same optimal solution in both cases. We also
discuss the effect of the quality of the initial solution on the convergence of the PGA.

Index Terms
Decentralized sensor networks, distributed detection, optimal fusion rule, genetic algorithms