Enhanced Optimization-based Node Localization Scheme for WSN

The location of a sensor node is crucial for several uses of WSN, including environmental sensing, search and rescue, geographical routing and tracking, and so on. The accuracy with which individual sensor terminals in a wireless sensor system can be located has a substantial bearing on the network's overall effectiveness. Using information about the locations of anchor nodes gathered from a variety of measures to pinpoint the unknown target nodes' placements is known as localization. This is a problem classed as NP-hard, which means it cannot be solved using classical deterministic methods. To overcome this difficulty in wireless sensor networks, presented an enhanced version of a swarm intelligence technique called the whale optimization method. This implementation, using a quasi-reflected-based learning method, fixes the problems with the original whale optimization approach. In order to ensure that the proposed metaheuristic performs as well as existing state-of-the-art metaheuristics, it is evaluated using the same network architecture and experimental settings. Proposed method use a Gaussian-modified RSSI to achieve a more accurate reading of the range and a new whale optimization algorithm to optimize the positioning of the nodes to boost the positioning accuracy, both of which are designed to compensate for the shortcomings of the positioning algorithm of both Received signal strength indicator (RSSI) ranging model. Based on the results of 20 separate benchmark function tests, the upgraded whale algorithm outperforms the whale optimization method and other swarm intelligence systems. The suggested location algorithm provides more precise placement than the original RSSI method. It is mentioned that the cluster intelligence algorithm has significant benefits over the currently implemented RSSI in positioning WSN nodes, and the improved algorithm that is described in this work has even more benefits compared to various cluster intelligence methods in tends to work the locating needs of real-world applications. The developed approach, as shown by simulation results, achieves better localization accuracy than the baseline whale optimization technique and other leading metaheuristics.