A decision theoretic approach to Gaussian sensor networks

Authors:
F. Davoli;M. Marchese;M. Mongelli
Affiliations:
DIST, Department of Communications, Computer and Systems Science, University of Genoa, Genova, Italy;DIST, Department of Communications, Computer and Systems Science, University of Genoa, Genova, Italy;DIST, Department of Communications, Computer and Systems Science, University of Genoa, Genova, Italy
Venue:
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Year:
2009

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Abstract

We consider the acquisition of measurements from a source, representing a physical phenomenon, by means of sensors deployed at different distances, and measuring random variables that are correlated with the source output. The acquired values are transmitted to a sink, where an estimation of the source has to be constructed, according to a given distortion criterion. In the presence of Gaussian random variables and a Gaussian vector channel, we are seeking optimum real-time joint source-channel encoder-decoder pairs that achieve a distortion sufficiently close to the theoretically optimal one, under a global power constraint, by activating only a subset of the sensors. The problem is posed in a team decision theoretic framework, and the optimal strategies are approximated by means of neural networks. We compare the solution with the results obtained by heuristically choosing a subset of the sensors on the basis of successive simulations under a fixed topology.