Enhancing Whale Optimization Algorithm with Levy Flight for coverage optimization in wireless sensor networks. (September 2021)

• Record Type: Journal Article
• Authors: Deepa, R.; Venkataraman, Revathi
• Is Part Of: Computers & electrical engineering. Volume 94(2021)
• Publication Date: 2021-09
• Subjects: Coverage optimization -- Whale Optimization algorithm -- Levy Flights -- K-Nearest Neighbor (centroid); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107359
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: In the present study, Levy Flight embedded with WOA was proposed. It helps in finding where the sensors have to be placed in the field. Twenty -five benchmark functions were used to evaluate the efficiency of the proposed algorithm. Node optimization percentage was found to be 33% using KNN (centroid) algorithm. Abstract: Coverage Optimization is one of the most essential pre-requisites in Wireless Sensor Networks (WSNs) which plays a significant and impactful role in the field of environmental monitoring, surveillance, socio-economic Cyber-networkings, etc. The Whale Optimization Algorithm (WOA) is a swarm intelligence based Search-Algorithm while browsing for an optimal solution, but, it suffers from the poor & inconsistent exploration problem and that causes trapping of local optima in randomly deployed nodes that fail to guarantee network coverage. To resolve the issue, an innovative study has been researched which presents an embedded coverage optimization WSN and is based on Levy Flight mechanism with WOA (LWOA) .This updates the current search of location for positioning the sensors in the field. This mechanism can enhance and balance the exploration ability of WOA, which allows trapping of the local optima. This thichnically enhanced and updated proposal LWOA is validated by 25 benchmark optimization functions and is compared with existing Particle Swarm Optimization and WOA. From the experimental results, it can be construed and proved that the performance of Levy WOA (LWOA) has significantly improved the global search capacity and increase the efficiency of convergence, which immensely enhances the efficacy of coverage of nodes inturn amplifying the overall performance of the network. Finally, by using the K-Nearest Neighbour KNN(centroid) approach nearly 33% of nodes were optimized. Graphical abstract: Image, graphical abstract