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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Ozobialu, chukwuma Emmanuel
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Computer Science & IT Engineering Mechanical Engineering Transportation

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Enhancing Indoor Positioning Accuracy with Ant Colony Optimization and Dual Clustering Oise, Godfrey; Nwabuokei, Onyemaechi Clement; Ozobialu, chukwuma Emmanuel; Jenarhome, Otega Prosper; Atake, Onoriode Michael; Nkem Belinda, Unuigbokhai; Babalola Eyitemi , Akilo
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.39452

Abstract

Indoor positioning systems are crucial for public safety, healthcare, and IoT, but Wi-Fi fingerprinting faces challenges such as signal interference, multipath effects, and high computational costs. These issues reduce positioning accuracy and make real-time localization difficult.This paper introduces an Ant Colony Optimization (ACO)-based dual clustering method to enhance Wi-Fi fingerprinting accuracy and efficiency. ACO performs coarse clustering by optimizing initial data groupings, while K-means refines clusters for improved precision. The Weighted K-Nearest Neighbor (WKNN) algorithm is then applied for real-time positioning by selecting the most similar signal sub-bases.Experiments show that the proposed method achieves 100% accuracy in building classification and 91% accuracy in floor classification. For latitude and longitude prediction, Random Forest and SVC outperform XGBoost, achieving MSE values of 0.0048 (latitude) and 0.0055 (longitude). The approach also reduces computational overhead by 93.51%, improving efficiency.The study presents a robust, scalable solution for indoor positioning and introduces the Dual Clustering Wi-Fi Localization Dataset (DCWiLD) for future research. Future work will focus on dataset balancing, BLE/UWB integration, and energy optimization for IoT applications.
Co-Authors Atake, Onoriode Michael Babalola Eyitemi , Akilo Jenarhome, Otega Prosper Nkem Belinda, Unuigbokhai Nwabuokei, Onyemaechi Clement Oise, Godfrey