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

Abstract

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.

Challenges in Automated Measurement of Pedestrian Dynamics

“…We are investigating to what extent the negative effects of MAC-address randomization can be mitigated to increase the accuracy of device identification, along with other techniques. Others are also developing techniques that may prove to be useful [18]. In addition, it remains unclear to what extent MAC randomization is effective [8,21], and to what extent it actually affects accurately counting pedestrian dynamics.…”

Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems

“…The IPIN competition is based on the EvAAL framework, which was conceived with the purpose of evaluating active and assisted living positioning systems through competitive benchmarking [1]. Unfortunately, most authors still do not share their datasets for different reasons, for instance: collecting a dataset is timeconsuming, as reported in many works [2][3][4], since it requires the mapping of reference points, data collection, and post-processing; the main focus of research teams is to develop novel positioning and localization methods, so they use their own datasets for developing and testing their systems, without the objective of sharing the datasets with the community; due to privacy reasons [5,6], since these datasets comprise Wi-Fi information, including Service Set Identifier (SSID) and Media Access Control (MAC) addresses of Access Points (APs).…”

An Experimental Assessment of People’s Location Efficiency Using Low-Energy Communications-Based Movement Tracking

“…This can be accomplished via recording of temporary tokens, or specific primary keys, instead of the exact MAC discovered address, then removing these when data present no more interest. The latest developments in Android-enabled devices include MAC address randomization capability, which makes tracking difficult, but does not affect measurement of traveler densities for transport demand analysis [39][40][41].…”