Minimum mean square error (MMSE) decoding in a large-scale sensor network which employs distributed quantization is considered. Given that the computational complexity of the optimal decoder is exponential in the network size, we present a framework based on Bayesian networks for designing a near-optimal decoder whose complexity is only linear in network size (hence scalable). In this approach, a complexity-constrained factor graph, which approximately represents the prior joint distribution of the sensor outputs, is obtained by constructing an equivalent Bayesian network using the maximum likelihood (ML) criterion. The decoder executes the sum-product algorithm on the simplified factor graph. Our simulation results have shown that the scalable decoders constructed using the proposed approach perform close to optimal, with both Gaussian and non-Gaussian sensor data.

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.