Sensor Selection for Target Tracking in Wireless Sensor Networks with Uncertainty

TL;DR

This paper introduces a low-complexity sensor selection method for uncertain wireless sensor networks, balancing the number of sensors used and estimation accuracy, demonstrated through numerical results.

Contribution

A novel mutual information upper bound based sensor selection scheme that is computationally efficient and performs comparably to existing methods.

Findings

The MIUB-based scheme achieves similar estimation performance to MI-based methods.

The multiobjective optimization provides diverse sensor selection strategies.

Numerical results offer insights into trade-offs in sensor selection.

Abstract

In this paper, we propose a multiobjective optimization framework for the sensor selection problem in uncertain Wireless Sensor Networks (WSNs). The uncertainties of the WSNs result in a set of sensor observations with insufficient information about the target. We propose a novel mutual information upper bound (MIUB) based sensor selection scheme, which has low computational complexity, same as the Fisher information (FI) based sensor selection scheme, and gives estimation performance similar to the mutual information (MI) based sensor selection scheme. Without knowing the number of sensors to be selected a priori, the multiobjective optimization problem (MOP) gives a set of sensor selection strategies that reveal different trade-offs between two conflicting objectives: minimization of the number of selected sensors and minimization of the gap between the performance metric (MIUB and FI) when all the sensors transmit measurements and when only the selected sensors transmit their measurements based on the sensor selection strategy. Illustrative numerical results that provide valuable insights are presented.