Incremental distributed identification of Markov random field models in wireless sensor networks

Authors:
Anand Oka;Lutz Lampe
Affiliations:
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada;Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
Venue:
IEEE Transactions on Signal Processing
Year:
2009

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Abstract

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.