Non-linear coding and decoding strategies exploiting spatial correlation in wireless sensor networks
The authors consider the acquisition of measurements from a source, representing a physical phenomenon, by means of sensors deployed at different distances, and measuring random variables (r.v.'s) that are correlated with the source output. The acquired values are transmitted over a wireless channel 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 (r.v.'s) and a Gaussian vector channel, the authors 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 resource 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. The analysis investigates the generalisation capabilities of the proposed approach, by showing insights into the structure of the problem. The surprising outcome is that a quasi-static application of the approach reveals to be sufficient to maintain quasi-optimal performance under a dynamic environment (e.g. with respect to nodes' positions).
