Enhancing Indoor Positioning Accuracy with Ant Colony Optimization and Dual Clustering

Authors

  • Godfrey Oise Department of Computing, Wellspring University, Edo State, Nigeria
  • Onyemaechi Clement Nwabuokei Department of Computer Science, Delta State College of Education Mosogar https://orcid.org/0000-0003-3275-9954
  • chukwuma Emmanuel Ozobialu Igbinedion University Okada, Edo State
  • Otega Prosper Jenarhome Computer science Department Delta state university, Abraka, Delta State
  • Onoriode Michael Atake Western Delta University, Oghara Delta State
  • Unuigbokhai Nkem Belinda Department of Computing, Wellspring University, Edo State, Nigeria
  • Akilo Babalola Eyitemi Department of Computing, Wellspring University, Edo State, Nigeria

DOI:

https://doi.org/10.26740/vubeta.v2i3.39452

Keywords:

Indoor Positioning, Wi-Fi Fingerprinting, Weighted K-Nearest Neighbor, Ant Colony Optimization, Dual Clustering

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.

Author Biographies

Godfrey Oise, Department of Computing, Wellspring University, Edo State, Nigeria

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Oise Godfrey Perfectson is a lecturer in the Department of Computing, Wellspring University, Benin City, Edo State, Nigeria. He received the BSc and M.Sc from the University of Benin in 2019 and 2022 respectively. He is mainly researching software engineering, artificial intelligence, and information technology. He can be contacted at

Email: godfrey.oise@wellspringuniversity.edu.ng.

Orcid: https://orcid.org/0009-0006-4393-7874

Onyemaechi Clement Nwabuokei, Department of Computer Science, Delta State College of Education Mosogar

mceclip1-1d2ce1a447e001d2f5176993046b7fa6.png

Clement Nwabuokei, Computer Science Lecturer at Delta State College of Education Mosogar, Delta State Nigeria. B.Sc, M.Sc Computer Science ,University of Benin,  2014 and 2019 respectively, PGDE (University of Port Harcourt , 2023) Diploma in computer Engineering (University of Benin, 2007) Research Interest in Software Engineering, Expert Systems, Email: clemcino04@gmail.com, Orcid: https://orcid.org/0000-0003-3275-9954

chukwuma Emmanuel Ozobialu, Igbinedion University Okada, Edo State

University Okada, Edo State, Nigeria

Otega Prosper Jenarhome, Computer science Department Delta state university, Abraka, Delta State

Computer science Department, Delta state university, Abraka, Nigeria

Onoriode Michael Atake, Western Delta University, Oghara Delta State

Western Delta University, Oghara Delta State, Nigeria

Unuigbokhai Nkem Belinda, Department of Computing, Wellspring University, Edo State, Nigeria

Department of Computing, Wellspring University, Edo State, Nigeria

Akilo Babalola Eyitemi , Department of Computing, Wellspring University, Edo State, Nigeria

Department of Computing, Wellspring University, Edo State, Nigeria

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2025-08-29

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[1]
G. Oise, “Enhancing Indoor Positioning Accuracy with Ant Colony Optimization and Dual Clustering”, Vokasi Unesa Bull. Eng. Technol. Appl. Sci., vol. 2, no. 3, pp. 516–530, Aug. 2025.

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Vol. 2 No. 3 (2025)

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Copyright (c) 2025 Godfrey Oise, Onyemaechi Clement Nwabuokei, chukwuma Emmanuel Ozobialu, Otega Prosper Jenarhome, Onoriode Michael Atake

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