Linear and non-linear strategies for power mapping in Gaussian sensor networks

This paper deals with non-linear coding-decoding strategies for Gaussian sensor networks that obey a global power constraint and are decentralized (each sensor\´s decision is based solely on the variable it observes). The sensors and the sink act as the members of a team, i.e., they possess different information and they share a common goal, which consists in minimizing the expected distortion on the variables of interest. As the inherent power allocation, derived in "static" conditions (stationarity of the stochastic environment, fixed topology), reveals to be optimal, the main interest is to analyze its robustness to variable system conditions. To this aim, this paper goes deep inside the generalization capabilities of the proposed approach, by showing some interesting insights into the structure of the problem. The overall surprising outcome is that a quasi-static application of the approach reveals to be sufficient to maintain suboptimal performance even under a dynamic environment.