An Online Method for Estimating the Wireless Device Count via Privacy-Preserving Wi-Fi Fingerprinting

Initially envisioned to accelerate association of mobile devices in wireless networks, broadcasting of Wi-Fi probe requests has opened avenues for researchers and network practitioners to exploit information sent out in this type of frames for observing devices' digital footprints and for their tracking. One of the applications for this is crowd estimation. Noticing the privacy risks that this default mode of operation poses, device vendors have introduced MAC address randomization-a privacy preserving technique by which mobile devices periodically generate random hardware addresses contained in probe requests. In this paper, we propose a method for estimating the number of wireless devices in the environment by means of analyzing Wi-Fi probe requests sent by those devices and in spite of MAC address randomization. Our solution extends previous work that uses Wi-Fi fingerprinting based on the timing information of probe requests. The only additional information we extract from probe requests is the MAC address, making our method minimally privacy-invasive. Our estimation method is also nearly real-time. We conduct several experiments to collect wireless measurements in different static environments and we use these measurements to validate our method. Through an extensive analysis and parameter tuning, we show the robustness of our method.