Wi-Fi 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. For identifying
unique users, previous works proposed capturing the media access
control (MAC) address of the signal emitted from a user’s device,
while information elements(IE)-based MAC de-randomization meth-
ods were presented to mitigate the impairment caused by random
MAC. However, the IE types of different Wi-Fi devices are not
consistently differentiated, resulting in incorrect identification when
using IE-based MAC de-randomization methods. In addition, for
pervasively locating users, typical Wi-Fi fingerprinting approaches
are constrained by the requirement of densely pre-deployed Wi-Fi
stations, which contradicts 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, a highly pervasive indoor localization solution, may be
implemented on a standard mobile Wi-Fi station without the need for extensive pre-deployment stations. Additionally, a
customized neural network model has been proposed to achieve fine-grained localization accuracy. DR.LIE performs MAC
de-randomization using a tailor-designed algorithm, named the spatio-temporal location, IE information and clustering-
based (LIC) algorithm, to identify users with random MAC addresses. Comprehensive experiments are conducted to
evaluate the performance of the proposed MS.Id. Experimental results demonstrate that MS.Id outperforms previous IE-
based user identification methods and multi-station localization techniques.