Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments

Abstract

Presently, various real time applications has been developed using smart systems such as smart cities, smart homes, smart transportation, etc. The use of smart sensors in those systems leads to the generation of different kinds of multimedia data like images, videos, audios, and so on. To acquire multimedia data from smart sensor environments, Wireless Sensor Networks (WSN) has been employed, which is an integral part of smart system which helps to maintain connectivity and coverage. In WSN, the major challenging issue is to process the massive amount of multimedia data which leads to maximum energy utilization. Clustering is an energy efficient way of organizing the network in a systematic way for proper load distribution and maximize network lifetime. To facilitate the optimal selection of Cluster Heads (CHs), in this paper, we propose an Improved Artificial Bee colony optimization based ClusTering(IABCOCT) algorithm by utilizing the merits of Grenade Explosion Method (GEM) and Cauchy Operator. This incorporation of GEM and Cauchy operator prevents the Artificial Bee Colony(ABC) algorithm from stuck into local optima and improves the convergence rate. The benefits of GEM and Cauchy operator are embedded into the Onlooker Bee and scout bee phase for phenomenal improvement in the degree of exploitation and exploration during the process of CH selection. The simulation results reported that the IABCOCT algorithm outperforms the state of art methods like Hierarchical Clustering-based CH Election (HCCHE), Enhanced Particle Swarm Optimization Technique (EPSOCT) and Competitive Clustering Technique (CCT) interms of different measures such as throughput, packet loss, delay, energy consumption and network lifetime.