With the widespread adoption of smartphones and other wearable devices nowadays, the massive amount of transmitted wireless signals left behind by the users provides a significant potential source of time and location information regarding human mobility. Furthermore, WiFi and Bluetooth wireless signal strength measurements have emerged as a promising solution for delivering proximity services to users. Accordingly, the present study proposes a system designated as WiTrack for tracking human-to-human mobility relationships in indoor environments based on the correlation between their wireless fingerprints. In particular, the mobility similarity between each pair of individuals is evaluated using the signal power features observed by a set of scanners deployed at different locations (i.e., spatial features) over time (i.e., temporal features). A higher similarity value is taken to indicate a more similar mobility behavior of the users. The feasibility of WiTrack is demonstrated using a testbed built in the corridor of a university campus.
|Title of host publication||2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)|
|Publication status||Published - 4 Nov 2019|
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