Comparative Analysis of Optimization Algorithms for Node Localization in Two Dimensional and Three Dimensional Wireless Sensor Networks

Accurate node localization is a fundamental challenge in Wireless Sensor Networks (WSNs), directly influencing data integrity, network efficiency, and the performance of location-dependent applications. This study presents a comprehensive comparative analysis of 52 optimization algorithms, including swarm intelligence, evolutionary computation, and bio-inspired techniques, applied to both two-dimensional (2D) and three-dimensional (3D) localization scenarios. The Fruit Fly Optimization Algorithm (FFOA) demonstrated the highest precision among the metaheuristic methods for 2D WSNs attaining a mean localization error of 9.2%, attributed to its lightweight search strategy and low memory overhead, though its performance degrades in networks with complex topologies. Additionally, an obstacle-aware localization approach for 3D WSNs achieved the most accurate results overall, with a localization error of 6.7%, effectively addressing scenarios involving significant physical obstructions at the expense of increased computational complexity. The findings underscore critical trade-offs between accuracy, convergence rate, and computational efficiency, offering valuable guidance for selecting suitable localization strategies based on application-specific constraints and performance requirements in WSN deployments.