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%.
Original language | English |
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Journal | IEEE Internet of Things Journal |
DOIs | |
Publication status | E-pub ahead of print - 13 Jan 2025 |
Scopus Subject Areas
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications
User-Defined Keywords
- Fingerprinting
- Indoor Localization
- MAC De-randomization
- User Identification
- Wi-Fi Mobile Single Station