Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)

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A Comparative Analysis of AI-assisted WiFi and BLE Indoor Positioning Systems

Authors
Yitong Wu1, *
1School of Electrical Engineering and Telecommunications, University of New South Wales, UNSW Sydney, NSW, 2052, Kensington, Australia
*Corresponding author. Email: yitong.wu1@student.unsw.edu.au
Corresponding Author
Yitong Wu
Available Online 18 February 2026.
DOI
10.2991/978-94-6463-986-5_51How to use a DOI?
Keywords
Indoor positioning; Deep Learning; RSSI; Bluetooth Low Energy; WiFi
Abstract

Indoor positioning has become an on-going issue in recent years. Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL) algorithms assisted WiFi and Bluetooth Low Energy (BLE) fingerprinting indoor positioning systems are regarded as common techniques. This work conducts a comparative analysis of some of the state-of-the-art ML and DL algorithms that can be used to process fingerprinting data, as well as the trade-offs when applying them in different scenarios. This includes supervised and unsupervised machine learning models, and an innovative method combining Deep Neutral Network (DNN) and vector embedding. The result shows that vector embedding using i-vectors conducts the most accurate performance after model adaptation, supervised machine learning techniques like Support Vector Machine (SVM) can be regarded as a balance between accuracy and efficiency, unsupervised machine learning technique Density Based Spatial Clustering of Applications with Noise (DBSCAN) is more adaptable when there is much noise, but become less precise when there exists dynamic objects.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
Series
Advances in Engineering Research
Publication Date
18 February 2026
ISBN
978-94-6463-986-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-986-5_51How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yitong Wu
PY  - 2026
DA  - 2026/02/18
TI  - A Comparative Analysis of AI-assisted WiFi and BLE Indoor Positioning Systems
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 491
EP  - 500
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_51
DO  - 10.2991/978-94-6463-986-5_51
ID  - Wu2026
ER  -