Real-time prediction of length of stay using passive Wi-Fi sensing

• Main Authors: LE, Truc Viet, SONG, Baoyang, WYNTER, Laura
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Wireless fidelity; Mobile handsets; Probes; Real-time systems; Servers; Sensors; Support vector machines; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/3958; https://ink.library.smu.edu.sg/context/sis_research/article/4960/viewcontent/Real_timePredictionLoS_WiFi_2017.pdf
• Institution: Singapore Management University

The proliferation of wireless technologies in today's everyday life is one of the key drivers of the Internet of Things (IoT). In addition to being an enabler of connectivity, the vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization of the devices themselves. Indeed, in order to discover and automatically connect to known Wi-Fi networks, mobile devices have to scan and broadcast the so-called probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is the ability to track and analyze human behaviors in real-time directly from the patterns observed from their Wi-Fi-enabled devices. In this paper, we develop such a system to obtain these Wi-Fi signatures in a completely passive manner and use the Wi-Fi features it captures within a set of adaptive machine learning techniques to predict in real-time the expected length of stay (LOS) of the device owners at a specific location.