A Particle Swarm Optimization Framework for Accurate Localization and Tracking in Wireless Sensor Networks

Wireless sensor networks (WSNs) are challenged by energy constraints, environmental factors, and dynamic conditions that make it difficult to locate a target accurately and track it efficiently. In this paper, we demonstrate how particle swarm optimization (PSO) can be used to improve tracking and localization accuracy and computing efficiency. To decrease localization errors, a 2D hyperbolic algorithm is combined with PSO‐based corrections. In experiments, both the localization and tracking performance of PSO‐based methods were superior to traditional approaches. On the basis of the proposed framework, the L3 and T2 methods demonstrate reduced processing time and comparable or improved accuracy over increasing particle counts, making them suitable for real‐time WSN applications.