Self-organization of Nodes using Bio-Inspired Techniques for Achieving Small World Properties

TL;DR

This paper presents a bio-inspired, local-rule-based algorithm for self-organizing wireless sensor networks to enhance connectivity and reduce average path length without requiring global network knowledge.

Contribution

It introduces a novel bio-inspired approach that enables nodes to self-organize and improve network properties using only local interactions, without global information.

Findings

Reduced average path length in simulated networks

Decreased number of disconnected components

Effective with simple local rules

Abstract

In an autonomous wireless sensor network, self-organization of the nodes is essential to achieve network wide characteristics. We believe that connectivity in wireless autonomous networks can be increased and overall average path length can be reduced by using beamforming and bio-inspired algorithms. Recent works on the use of beamforming in wireless networks mostly assume the knowledge of the network in aggregation to either heterogeneous or hybrid deployment. We propose that without the global knowledge or the introduction of any special feature, the average path length can be reduced with the help of inspirations from the nature and simple interactions between neighboring nodes. Our algorithm also reduces the number of disconnected components within the network. Our results show that reduction in the average path length and the number of disconnected components can be achieved using very simple local rules and without the full network knowledge.