Energy Efficient Coverage Optimization in Mobile Wireless Sensor Network Using Grey Wolf Algorithm

Abstract

The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node's energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adeptly authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms. © 2023 IEEE.