A Particle Swarm Optimization‐Based Approach for Accurate Localization in Wireless Sensor Networks

ABSTRACT This paper presents a particle swarm optimization (PSO)‐based approach to enhance node localization accuracy in wireless sensor networks (WSNs). Traditional range‐based methods such as RSSI suffer from high positioning errors, while conventional DV‐Hop algorithms rely on assumptions that often fail under real‐world conditions. To overcome these limitations, the proposed method integrates a bounding box technique to constrain the initial search space, along with anticipatory and refinement strategies to address flip ambiguity. Additionally, inter‐node distance information, including distances between unknown nodes, is leveraged to further improve localization accuracy. Simulation results demonstrate that the proposed PSO‐based approach significantly reduces localization error, enhances accuracy and coverage, and lowers variance compared to standard DV‐Hop and Improved DV‐Hop (IDV‐Hop) algorithms. These improvements contribute to more accurate, reliable, and computationally efficient localization in WSNs.