Hybrid Red Fox and Improved Whale Optimization Algorithm-based Clustering scheme for improving network longevity in Wireless Sensor Networks

Abstract

Guaranteeing energy stability is highly challenging in Wireless Sensor Networks (WSNs) as it contributes towards an enhanced network lifespan. Clustering with improved energy efficacy is found to be an NP-hard optimization problem with the possibility of offering extended network lifespan. Several computational methods that comprise of nature inspired meta-heuristic as well as reinforcement schemes along with evolutionary algorithms are used for supporting effectual clustering in WSNs. The heuristic and meta-heuristic schemes of CH selection designed for dealing with optimization problems using empirical behaviour was identified to be ideal in better clustering process. In this paper, Hybrid Red Fox and Improved Whale Optimization Algorithm (HRFIWOA)-based Clustering scheme is proposed enhancing lifespan expectation and energy constancy in WSNs. This HRFIWOA is proposed with the global optimization competence of the Red Fox Optimization Algorithm (RFOA) and local optimization capability of Improved Whale Optimization Algorithm (IWOA). It specifically included IWOA into RFOA for achieving and sustaining a balance amid the stages of exploration and exploitation which is demanded for optimised CH selection. The adopted IWOA helps in better exploitation of the solution that comprises of information pertaining to the sensor nodes which can be potentially identified as CH in the network. The simulation results of the proposed HRFIWOA confirmed promising capability in improving the packet deliver rate by 20.96%, network lifespan by 18.54%, throughput by 17.62% with reduced latency of 19.28%, and minimized energy consumption of 22.39%, better than the competitive CH selection schemes used for investigation.