A novel approach to node coverage enhancement in wireless sensor networks using walrus optimization algorithm

Wireless Sensor Networks (WSNs) are crucial components of modern technology, supporting applications like healthcare, industrial automation, and environmental monitoring. This research aims to design intelligent and adaptive sensor networks by integrating metaheuristics with node coverage optimization in WSNs. By incorporating metaheuristics and optimizing node coverage, WSNs can become more resilient and robust, leading to the development of self-adapting, self-organizing networks capable of efficiently covering dynamic and diverse environments. This research introduces the Walrus Optimization Algorithm for Node Coverage Enhancement in WSNs, called the WaOA-NCEWSN technique. The primary goal of this technique is to optimize the coverage of a target region using a limited number of Sensor Nodes (SNs) and by improving their placement. The WaOA is inspired by walrus behaviours like feeding, migrating, breeding, escaping, roosting, and gathering in response to environmental signals. The WaOA-NCEWSN technique uses an objective function that defines the coverage ratio, representing the maximum probability of coverage in a 2D-WSN monitoring area. Comparative analysis with other models using 50, 75, 100, and 200 nodes shows that the WaOA-NCEWSN technique performs better. The compilation times for the WaOA-NCEWSN technique are 5.14s, 6.48s, 6.54s, and 7.47s for 50, 75, 100, and 200 nodes, respectively. Experimental results indicate that the WaOA-NCEWSN technique offers superior coverage performance compared to other models. • The WaOA-NCEWSN technique is a novel Walrus optimization algorithm that is based on the improvement of node coverage in WSN. WaOA is the primary objective of the WaOA-NCEWSN technique, which is to optimize the coverage of the target region by utilizing limited Sensor Nodes (S.N.s) and optimizing the positioning positions. • The WaOA in the WaOA-NCEWSN technique is derived from the characteristics of walruses, which receive critical signals to determine their feeding, migration, breeding, escape, roosting, and gathering behaviors. • The WaOA-NCEWSN technique generates an objective function that specifies the coverage ratio, which corresponds to the maximum probability ratio of the network's deployed 2D-WSN monitoring region. • The comparison of the WaOA-NCEWSN technique with other models that employ 50, 75, 100, and 200 nodes. The compilation time of the WaOA-NCEWSN technique is 5.14s, 6.48s, 6.54s, and 7.47s for 50, 75, 100, and 200 nodes, respectively.