Incremental Distributed Identification of Markov Random Field Models in Wireless Sensor Networks

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.