Coverage Area Maximization using MOFAC-GA-PSO Hybrid Algorithm in Energy Efficient WSN design

Abstract

Coverage area optimization is always a challenging task to configure an efficient Wireless Sensor Network (WSN). This article proposes an energy-efficient coverage area optimization technique of WSN using a novel hybrid algorithm, called MOFAC-GA-PSO (Minimum Overlapped Full Area Coverage using hybridized Genetic Algorithm-Particle Swarm Optimization) algorithm. The objectives of the article are maximization of coverage area, minimization of coverage hole as well as energy requirement. The above-mentioned three objectives had not been yet addressed combinedly with the existing literature. This limitation has been addressed in the proposed work with 100% area coverage. The result of the proposed algorithm is compared with the existing literature as well as with the individual meta-heuristic algorithms (i.e., GA and PSO) to prove the competence of the MOFAC-GA-PSO algorithm. To achieve the benefits of both optimizers, the GA was treated as a global optimizer while the PSO was treated as a local optimizer. The proposed research work achieves 100 percent area coverage with just 25 mobile WSN nodes, but the existing methodology can only provide a maximum of 91.26 percent of area coverage. In terms of energy efficiency, the network built by the proposed algorithm can last 11.06 days as contrasted to the performance of the existing paper, which is 6.33 days. So, a significant improvement concerning the maximization of coverage area as well as minimization of coverage hole, and energy requirement has been observed. Last, but not the least, a statistical analysis is carried out to justify the research for the required number of optimized WSN nodes.