Wireless sensor network coverage of improved sea lion algorithm

The base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well-known NP-hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above-mentioned problem is modeled by covering a new 2-D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI-SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI-SLOB scheme over the other state-of-the-art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management.