HGGRKO: An Optimized Hybrid Approach for Precision Node Localization in Wireless Sensor Networks

Localization, or position in Wireless Sensor Networks (WSNs), is one of the most challenging and crucial tasks in a range of tracking and monitoring applications. This problem is brought on by the need to disperse the network over large areas and provides recently acquired location data to unidentified devices. With conventional localization methods, scalability and computation time constraints are frequent problems. In this paper, a novel hybrid optimization strategy is proposed to enhance the precision and robustness of node localization within WSNs. The recently proposed HybridisedGreylag Goose Red Kite Optimization (HGGRKO) represents a hybrid optimization strategy that combines two efficient metaheuristic techniques from the Red Kite Optimization (RKO) and Greylag Goose Optimization (GGO) algorithms to accomplish the objective of the framework. The main objective of the HGGRKO-based architecture is to minimize the localization error between the detected and actual locations of each node in the WSN. The HGGRKO technique uses the exploration capabilities of the GGO algorithm and the exploitation capacities of the RKO algorithm to improve localization accuracy. The method selects anchor nodes carefully to further reduce localization errors. The HGGRKO algorithm can be used to reduce the number of nodes, boost coverage rates, and maintain network connections. To evaluate the effectiveness of the HGGRKO approach, MATLAB software is utilized. The findings demonstrate that the approach outperforms conventional optimization algorithms in terms of speed, localized node count, localization error minimization across a variety of anchor node counts, and execution time.