Research on Swarm Intelligence-Optimised Positioning Algorithms for Wireless Sensor Networks

Wireless sensor network (WSN) localization is a critical enabling technology for the Industrial Internet and smart grid applications. However, non-line-of-sight (NLOS) propagation and multipath interference often introduce significant localization errors. To address these challenges, this paper proposes an enhanced particle swarm optimization (PSO)-based localization algorithm incorporating adaptive inertia adjustment and region-aware correction. The proposed method effectively mitigates premature convergence and random fluctuations during the optimization process, thereby improving overall localization reliability. Experimental results demonstrate a 37.5% enhancement in localization accuracy, reducing the mean error from ±80 m to ±50 m. Field deployment in Jiangsu Power Grid substations further validates the algorithm's engineering reliability, yielding an annual cost saving of ¥3.7 million. These findings indicate that the proposed approach achieves both high localization precision and robust industrial applicability.