ACM Computing Surveys (CSUR)
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Statistical Inference
Call for papers: special issue on distributed source coding
Signal Processing - Special section: New trends and findings in antenna array processing for radar
Learning Bayesian Networks
Low-complexity coding and source-optimized clustering for large-scale sensor networks
ACM Transactions on Sensor Networks (TOSN)
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Error-resilient and complexity-constrained distributed coding for large scale sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
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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.