Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks

TL;DR

This paper presents a bio-inspired algorithm that uses local information to significantly improve small world properties in wireless ad hoc networks, reducing average path length while maintaining clustering.

Contribution

The study introduces a novel bio-inspired approach combining Lateral Inhibition and Flocking techniques to enhance small world properties in wireless networks with non-uniform node density.

Findings

Up to 40% reduction in average path length in high-density networks

Effective regional formation using local information techniques

Maintained clustering coefficient despite reduced path length

Abstract

It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve network wide characteristics. Studies have shown that Small World properties, primarily low average path length and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small world properties show a degree distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the average path length and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on average path length, clustering coefficient and connectivity.