Stochastic Sensor Scheduling for Energy Constrained Estimation in Multi-Hop Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) enable a wealth of new applications where\nremote estimation is essential. Individual sensors simultaneously sense a\ndynamic process and transmit measured information over a shared channel to a\ncentral fusion center. The fusion center computes an estimate of the process\nstate by means of a Kalman filter. In this paper we assume that the WSN admits\na tree topology with fusion center at the root. At each time step only a subset\nof sensors can be selected to transmit observations to the fusion center due to\na limited energy budget. We propose a stochastic sensor selection algorithm\nthat randomly selects a subset of sensors according to certain probability\ndistribution, which is opportunely designed to minimize the asymptotic expected\nestimation error covariance matrix. We show that the optimal stochastic sensor\nselection problem can be relaxed into a convex optimization problem and thus\nsolved efficiently. We also provide a possible implementation of our algorithm\nwhich does not introduce any communication overhead. The paper ends with some\nnumerical examples that show the effectiveness of the proposed approach.\n