Pervasive Indoor User Identification Leveraging Mobile Single Station Localization
Nie, Wendi Liu, Zexing Wang, Xiaoyang Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph Kee-Yin Xue, Chun Jason Gui, Guan
Nie, Wendi
Liu, Zexing
Wang, Xiaoyang
Duan, Yaoxin
Lam, Kam-Yiu
Liu, Kai
Ng, Joseph Kee-Yin
Xue, Chun Jason
Gui, Guan
Supervisor
Department
Computer Science
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Type
Journal Article
Date
2025
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Language
English
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Abstract
The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user’s device and using Information Elements (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely pre-deployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single station-based user identification (MS.Id) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. MS.Id includes mobile single station localization (MSL) and MAC de-randomization based on users’ spatio-temporal location and IE information (DR.LIE). MSL can be implemented on a standard mobile Wi-Fi station without extensive pre-deployment. DR.LIE performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that MS.Id outperforms previous IE-based user identification methods and multi-station localization techniques. MSL achieves a localization error of 1.15 meters which is better than multi-station with 12 APs of 1.40 meters. DR.LIE demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%.
Citation
W. Nie et al., "Pervasive Indoor User Identification Leveraging Mobile Single Station Localization," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3528447.
Source
IEEE Internet of Things Journal
Conference
Keywords
Wireless Fidelity, Location Awareness, Fingerprint Recognition, Object Recognition, Accuracy, Telecommunications, Internet of Things, Indoor Environment, Computer Science, User Experience
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Publisher
IEEE