Accurate and Efficient Wi‐Fi Indoor Localization via Improved Binary PSO‐Based AP Selection

ABSTRACT

Indoor localization using Wi‐Fi fingerprinting has gained significant attention due to its cost‐effectiveness and the widespread availability of Wi‐Fi infrastructure. However, the high dimensionality of data, along with signal fluctuations and redundant access points (APs), often degrades localization accuracy and increases computational complexity. This paper proposes an improved binary particle swarm optimization‐based feature selection strategy to identify the most informative APs for fingerprint‐based indoor localization. The approach integrates a novel hybrid transfer function combining V‐shaped and U‐shaped characteristics with an enhanced learning mechanism to balance exploration and exploitation while preserving population diversity. An improved weighted k‐nearest neighbours algorithm is employed to evaluate the selected AP subsets. Experiments conducted on the UJIIndoorLoc benchmark dataset demonstrate that the proposed method achieves an overall weighted mean localization error of 6.27 m while selecting approximately 85% fewer APs than the original feature set. The proposed AP selection strategy reduces computational overhead during localization, making the method suitable for deployment on resource‐constrained devices without compromising localization performance.