A Novel Approach to Node Coverage Enhancement in Wireless Sensor Networks Using Walrus Optimization Algorithm

Abstract

One of the present field's hotspots for study is the wireless sensor network coverage optimization challenge. By studying coverage optimization challenges, we may make the network more stable, reduce distributed redundancy of sensor nodes, enhance coverage, and use less energy. An approach to node coverage optimization based on WaOA (Walrus Optimization Algorithm) is presented, which aims to address the issues of low coverage and uneven distribution that arise when wireless sensor network (WSN) nodes are randomly distributed. To optimize the coverage of wireless sensor networks, a mathematical model must first be constructed. Next, we solve the WSN coverage optimization issue using the WaOA method. WaOA provides the best results when merging various sub-area network coverages. Based on 23 benchmark functions, simulation results demonstrate that WaOA outperforms several other well-known algorithms in terms of search accuracy and convergence speed. Simultaneously, Butterfly Optimization Algorithm (BOA), Seagull Optimization Algorithm (SOA), and Bald Eagle Search (BES) are the most widely utilized WaOA methods. This indicates that WaOA can attain more coverage than these algorithms in terms of the coverage optimization impact. Empirical findings demonstrate that WaOA may efficiently enhance sensor node coverage and enhance node distribution in WSN coverage optimization issues.