Cosntruction of a scalable decoder for a wireless sensor network using Bayesian networks

We consider minimum mean square error (MMSE) decoding in a dense sensor network where distributed quantization is used to improve the performance. In view of the exponential complexity of the optimal decoder, we present a framework based on Bayesian networks for designing a scalable, but near-optimal decoder. In this approach, a complexity- constrained factor graph is obtained by an algorithm which constructs an equivalent Bayesian network using the maximum likelihood (ML) criterion, based on a training set of sensor observations. Our simulation results show that, the scalable decoders constructed using the proposed approach preform close to optimal, with both Gaussian and non-Gaussian sensor data.