1 research outputs found
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.\u2019s) 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.\u2019s) and
a Gaussian vector channel, the authors are seeking optimum real-time joint source-channel encoder\u2013decoder 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\u2019 positions)