Cluster optimization in wireless sensor network based on optimized Artificial Bee Colony algorithm

Abstract

Wireless sensor networks (WSNs) have emerged as a potential research area owing to their wide range of applicability in various fields. Critical application areas of WSN include defence and military surveillance, weather monitoring, health care monitoring, and Internet of Things. Extensive research efforts have been made to improve energy and data delivery performance in WSN with different bio-inspired optimized clustering methodologies such as particle swarm optimization (PSO), and the bacterial foraging algorithm for optimization (BFAO). However, most constrained solutions are limited to data aggregation performance and enhance the energy efficiency of the network to some extent. Therefore, balancing energy and data delivery performance to a greater extent is crucial because of design limitations imposed on existing hierarchical solutions. This article introduces a novel clustering paradigm, namely optimal clustering using the Artificial Bee Colony (OCABC) algorithm, which improves energy efficiency based on a simplified and robust ABC algorithm. The central idea is to increase the network lifetime of the WSN by optimizing the cluster formation process. The implemented structured module of OCABC attempts to overcome challenges encountered in existing baselines. The extensive numerical analysis with respect to significant performance parameters assists in benchmarking the OCABC compared with the PSO and BFAO. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.