Adaptive Node Localization Method in Wireless Sensor Networks based on Mountain Gazelle Optimizer Algorithm

Abstract

In wireless sensor networks (WSNs), accurate node localization is critical for ensuring efficient network functionality, as it directly impacts communication, energy consumption, and network management. This paper aims to enhance node localization accuracy by developing a hybrid approach that leverages two bioinspired optimization algorithms: the Mountain Gazelle Optimizer (MGO) and the Crayfish Optimization Algorithm (COA). The research method combines the exploration and exploitation capabilities of these algorithms to optimize the positions of unknown (target) nodes using known (anchor) nodes. The proposed technique was tested across multiple WSN deployment scenarios and compared with traditional optimization methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). Experimental results demonstrate that the MGO-based approach achieves superior localization accuracy, reduces computational overhead, and increases the number of accurately localized nodes, highlighting its potential for improving WSN performance.