A Novel Energy Aware Node Clustering Algorithm for Wireless Sensor Networks Using a Modified Artificial Fish Swarm Algorithm

TL;DR

This paper introduces a new energy-aware node clustering algorithm for wireless sensor networks, based on a modified Artificial Fish Swarm Algorithm, enhancing convergence speed and balancing local and global searches.

Contribution

It proposes a novel AFSA-based clustering algorithm with improved convergence and search balance for wireless sensor networks.

Findings

Outperforms existing clustering techniques in simulations.

Achieves longer network lifetime.

Faster convergence in clustering tasks.

Abstract

Clustering problems are considered amongst the prominent challenges in statistics and computational science. Clustering of nodes in wireless sensor networks which is used to prolong the life-time of networks is one of the difficult tasks of clustering procedure. In order to perform nodes clustering, a number of nodes are determined as cluster heads and other ones are joined to one of these heads, based on different criteria e.g. Euclidean distance. So far, different approaches have been proposed for this process, where swarm and evolutionary algorithms contribute in this regard. In this study, a novel algorithm is proposed based on Artificial Fish Swarm Algorithm (AFSA) for clustering procedure. In the proposed method, the performance of the standard AFSA is improved by increasing balance between local and global searches. Furthermore, a new mechanism has been added to the base algorithm for improving convergence speed in clustering problems. Performance of the proposed technique is compared to a number of state-of-the-art techniques in this field and the outcomes indicate the supremacy of the proposed technique.