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

TL;DR

This paper introduces a stochastic sensor scheduling method for energy-limited wireless sensor networks, optimizing sensor selection to improve estimation accuracy without extra communication costs.

Contribution

It presents a convex optimization-based stochastic sensor selection algorithm tailored for energy-constrained multi-hop WSNs with tree topology.

Findings

Optimized sensor selection reduces estimation error.

Convex relaxation enables efficient computation.

Algorithm implementation avoids additional communication overhead.

Abstract

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