Pervasive Indoor User Identification Leveraging Mobile Single-Station Localization

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 element (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 predeployed 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’ spatiotemporal location and IE information (DR.LIE). MSL can be implemented on a standard mobile Wi-Fi station without extensive predeployment. 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 multistation localization techniques. MSL achieves a localization error of 1.15 m which is better than multistation with 12 APs of 1.40 m. DR.LIE demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%.