H2HCO-BT+: Improving Wireless Sensor Network Node Localization Accuracy by Hybrid Hunting Cat Optimization and Battle Tactics Optimization

Accurate sensor node localization is essential to the efficient operation of Wireless Sensor Networks (WSNs), which are essential in many applications. . Determining the geographical location of every network node is necessary. Sensor node localization in WSNs is important for a number of applications, including military applications that need to track the movement of adversaries. The main goal here is to find all the coordinates of target nodes with help from anchor nodes. , people have gotten excited about using optimization tricks to make WSN node localization better. This paper comes up with a smart optimization approach by mixing Hunting Cat Optimization with Battle Tactics Optimization. This new method fixes issues that older methods had and paves the way for WSNs to work more and be tougher. The tests show that this new scheme does better than current optimization localization strategies when it comes to keeping node locations consistent and accurate. The suggested algorithm's superiority is demonstrated by the experimental results for a range of optimization problems. The study is scalable to large-scale networks because of its distributed design and metaheuristic-based methodology. GOA, KOA, and GWO are three metaheuristic algorithms that have demonstrated noteworthy performance in terms of coverage rates (CR), with respective coverage rates of 96.5%, 87.6%, and 93.9%. Our simulations show that H2HCO-BT+ performs better than other methods, with an average improvement rate of 99.6%. These techniques have shown themselves to be efficient in solving sensor node localization challenges.