An Efficient Localization Approach in Wireless Sensor Networks Using Chicken Swarm Optimization

Wireless sensor networks (WSN) have piqued the interest of many academics in recent years, and this technology has potential applications in a variety of sectors. A WSN is made up of nodes that are placed in a target environment and interact with it to detect data. Node deployment, clustering, data aggregation, energy consumption, and localization are all issues that WSNs confront. Localization is one of the most pressing concerns confronting the WSN. To solve the challenge of localization, many measuring methods and algorithms have been proposed. In order to use executing metaheuristics, several scholars have phrased the localization issue as an optimization problem. In terms of performance and accuracy, nature-inspired metaheuristics are viable alternatives to traditional approaches. One of the swarm-based metaheuristics we’re interested in is Chicken Swarm Optimization (CSO). Because of its simplicity and effectiveness, CSO has shown to be quite effective in resolving localization challenges in WSNs. In this research, we present a method for reducing localization error in WSNs and improving CSO performance and efficiency. The suggested method’s efficacy was evaluated and compared to traditional CSO and Particle Swarm Optimization algorithms. The collected findings suggest that the proposed technique lowers localization error in WSNs in a cost-effective way. They also demonstrate that the suggested technique compares with and even exceeds the alternatives.