Published on December 2019 | Wireless Sensor Network

OBC-WOA: Opposition-Based Chaotic Whale Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Network

A large number of small sensors in the Wireless Sensor Network (WSN) can be an efficient tool for data collection in a range of environments. Each sensor transfers the information to the basic unit that transmits the information to the end user. The objective of clustering is to separate the network into sectors with a cluster head (CH). The job is to collect, aggregate and transmit cluster heads to the base station. Energy Efficient Clustering has been used widely for energy conservation in wireless sensor networks (WSNs) and also preserves the restricted energy resources of their sensors. Cluster heads (CHs) perform a major part and exhaust power faster than other member nodes in a distributed WSN. Opposition-based Chaotic Whales Optimization algorithm (OBC-WOA) is meta-heuristic optimization algorithm which has recently been proposed in opposition. It simulates humpback whales ' social behaviour. OBC-WOA produces randomly its population during exploration and exploitation stages, like other population-based systems, which can produce values far from the optimal alternative or block the development of local optima. The revised algorithm known as Opposition-based Chaotic Whale Optimization Algorithm (OBC-WOA) is designed to increase solution precision and reliability. The OBC-WOA uses a technique based on opposition to improve the efficiency of OBC-WOA. The OBC-WOA is screened with the initial WOA algorithm and other meta-heuristic techniques. The performance of the proposed approach is evaluated in terms of energy consumption, throughput, packet delivery ratio and network life time. When compared with the existing method the proposed method is 12% better than Whale Optimization algorithm (WOA), 25% better than Gravitational Search Algorithm (GSA), 40% better than Particle Swarm Optimisation (PSO) and 8% better than Fuzzy K-Means and Centralized Mid-point Algorithm (FKM-CMA).