A Subset Selection Algorithm for Wireless Sensor Networks

TL;DR

This paper introduces a subset selection algorithm for wireless sensor networks that minimizes power consumption while maintaining a specified distortion level, leveraging channel state information and correlated fading models.

Contribution

It proposes a novel subset selection method based on channel information to optimize power use in WSNs while controlling estimation distortion.

Findings

The subset size distribution is derived analytically.

The algorithm improves power efficiency under correlated fading channels.

Results demonstrate effective power reduction with maintained accuracy.

Abstract

One of the main challenges facing wireless sensor networks (WSNs) is the limited power resources available at small sensor nodes. It is therefore desired to reduce the power consumption of sensors while keeping the distortion between the source information and its estimate at the fusion centre (FC) below a specific threshold. In this paper, given the channel state information at the FC, we propose a subset selection algorithm of sensor nodes to reduce the average transmission power of the WSN. We assume the channels between the source and the sensors to be correlated fading channels, modeled by the Gilbert-Elliott model. We show that when these channels are known at the FC, a subset of sensors can be selected by the FC such that the received observations from this subset is sufficient to estimate the source information at the FC while maintaining the distortion between source information and its estimate below a specific threshold. Through analyses, we find the probability distribution of the size of this subset and provide results to evaluate the power efficiency of our proposed algorithm.