The value of feedback for decentralized detection in large sensor networks
Author(s)Tay, Wee Peng; Tsitsiklis, John N.
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We consider the decentralized binary hypothesis testing problem in networks with feedback, where some or all of the sensors have access to compressed summaries of other sensors' observations. We study certain two-message feedback architectures, in which every sensor sends two messages to a fusion center, with the second message based on full or partial knowledge of the first messages of the other sensors. Under either a Neyman-Pearson or a Bayesian formulation, we show that the asymptotically optimal (in the limit of a large number of sensors) detection performance (as quantified by error exponents) does not benefit from the feedback messages.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 6th International Symposium on Wireless and Pervasive Computing (ISWPC), 2011
Institute of Electrical and Electronics Engineers (IEEE)
Tay, Wee Peng, and John N. Tsitsiklis. “The Value of Feedback for Decentralized Detection in Large Sensor Networks.” 6th International Symposium on Wireless and Pervasive Computing (ISWPC), 2011. 1–6.
Author's final manuscript