Structural Changes in Data Communication in Wireless Sensor Networks

TL;DR

This paper investigates how different data communication strategies in wireless sensor networks affect network measures, especially shortest path length, under various perturbations, using information-theoretic metrics.

Contribution

It evaluates the sensitivity of complex network measures in wireless sensor networks across four communication models with perturbations, employing information theory tools.

Findings

Shortest path length is sensitive to network perturbations.

Different communication strategies exhibit varying robustness to changes.

Information-theoretic measures effectively quantify network measure sensitivities.

Abstract

Wireless sensor networks are an important technology for making distributed autonomous measures in hostile or inaccessible environments. Among the challenges they pose, the way data travel among them is a relevant issue since their structure is quite dynamic. The operational topology of such devices can often be described by complex networks. In this work, we assess the variation of measures commonly employed in the complex networks literature applied to wireless sensor networks. Four data communication strategies were considered: geometric, random, small-world, and scale-free models, along with the shortest path length measure. The sensitivity of this measure was analyzed with respect to the following perturbations: insertion and removal of nodes in the geometric strategy; and insertion, removal and rewiring of links in the other models. The assessment was performed using the normalized Kullback-Leibler divergence and Hellinger distance quantifiers, both deriving from the Information Theory framework. The results reveal that the shortest path length is sensitive to perturbations.