SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

An Approximate Dynamic Programming Approach for Commuications Constrained Inference

Author(s)

Jason Williams	MIT
John Fisher	MIT
Alan Willsky	MIT

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Abstract

Resource management in distributed sensor networks is a challenging problem. This can be attributed to the fundamental tradeoff between the value of information contained in a distributed set of measurements versus the energy costs of acquiring measurements, fusing them into a model of uncertainty, and transmitting the resulting model. Communications is commonly the highest contributor among these costs, typically by orders of magnitude. Failure to consider this tradeoff can significantly reduce the operational lifetime of a sensor network. While a variety of methods have been proposed that treat a subset of these issues, the approaches are indirect and usually consider at most a single time step. In the context of target tracking with a distributed sensor network we propose an approximate dynamic programming approach which integrates the value of information and the cost of transmitting data over a rolling time horizon.