HSRKO: A Hybrid Metaheuristic Approach for RSSI‐Based Node Localization in 3D Wireless Sensor Networks

Various localization technologies have been studied for wireless sensor networks (WSNs) in three‐dimensional topology. Despite this, the development of new localization algorithms to improve the accuracy of node positioning and the sophistication of existing technologies remains a significant research problem. The hybrid sandpiper red kite optimization (HSRKO) framework combines hierarchical ranking strategies with stochastic optimization to address the challenges posed by dynamic and uncertain environments in WSNs. In this paper, an advanced heuristic, the HSRKO algorithm, is developed to tune the parameters of the distance estimation technique. An objective function for minimizing the average localization error is formulated by optimizing the positions of unknown nodes using distances computed from RSSI. The experimental simulation is conducted to compare the developed and conventional models, demonstrating that the presented model is more effective. Simulation results indicate that HSRKO consistently achieves lower average localization errors and up to 100% localization efficiency under controlled noise conditions and sufficient anchor density. Thus, it was confirmed that the proposed HSRKO algorithm improves localization accuracy while demonstrating favorable energy consumption characteristics under identical simulation conditions. The algorithm exhibits robust performance across various network topologies and deployment scenarios, including irregular three‐dimensional terrains.