Energy and Distortion Minimization in “Refining” and “Expanding” Sensor Networks

Abstract

We consider two instances of different models of sensor networks, introduced in (IEEE Signal Process Mag 23(4):70-83, 2006) and termed “refining” and “expanding,” respectively. The first one refers to 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. The second model represents a “rich” communication infrastructure, where all sensor readings potentially bring fresh information to the sink. In both cases, we want to investigate coding strategies 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. In the presence of Gaussian sources and a Gaussian vector channel, with quadratic distortion function, the coding/decoding strategies are sought by means of approximating functions based on back-propagation neural networks. We compare the nonlinear team solutions with the best linear ones.