Sensor selection strategies for state estimation in energy constrained wireless sensor networks

Authors:
Yilin Mo;Roberto Ambrosino;Bruno Sinopoli
Affiliations:
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA;Dipartimento per le Tecnologie, Universití degli Studi di Napoli Parthenope, Centro Direzionale, Isola C4, 80143, Napoli, Italy;Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Automatica (Journal of IFAC)
Year:
2011

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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 base station. The base station computes an estimate of the process state by means of a Kalman filter. In this paper we assume that, at each time step, only a subset of all sensors are selected to send their observations to the fusion center due to channel capacity constraints or limited energy budget. We propose a multi-step sensor selection strategy to schedule sensors to transmit for the next T steps of time with the goal of minimizing an objective function related to the Kalman filter error covariance matrix. This formulation, in a relaxed convex form, defines an unified framework to solve a large class of optimization problems over energy constrained WSNs. We offer some numerical examples to further illustrate the efficiency of the algorithm.