A Decision Theoretic Approach to Gaussian Sensor Networks

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.