Stochastic Sensor Scheduling for Energy Constrained Estimation in Multi-Hop Wireless Sensor Networks
Yilin Mo, Emanuele Garone, Alessandro Casavola, Bruno Sinopoli

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…
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