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.
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Notes
- 1.
While, with just four adapters, we do not obtain full channel coverage and thus may miss frames, this leaner design makes the system portable, e.g., to be carried in a backpack.
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Appendices
Appendix A Data Exploration for Parameter Tuning
1.1 A.1 Analysis of the Algorithm Bootstrap Time
Table A.1 lists overlap-MAC and the number of all valid created clusters, cluster-DB, for different bootstrapTime values used when initializing the cluster database with Algorithm 3 for two datasets. The results in the table show that overlap-MAC decreases or remains the same whereas cluster-DB decreases as we extend bootstrapTime. Thus, creating the initial cluster database using a larger subset of data (longer bootstrapTime) ensures fewer errors in signature clustering. In addition, as shown in Fig. 6, the estimated number of devices is almost the same for different bootstrapTime. This suggests that smaller bootstrapTime leads to creating extra clusters. However, since our device counting method is able to cope with ”stale” clusters the end result is that the estimates for the number of detected devices do not differ by more than 1, as already explained in Sect. 3.3. As our trimmed flight datasets contain approximately 2.5 h of measurements, we set bootstrapTime to 30 min.
Appendix B Algorithms
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Torkamandi, P., Kärkkäinen, L., Ott, J. (2021). An Online Method for Estimating the Wireless Device Count via Privacy-Preserving Wi-Fi Fingerprinting. In: Hohlfeld, O., Lutu, A., Levin, D. (eds) Passive and Active Measurement. PAM 2021. Lecture Notes in Computer Science(), vol 12671. Springer, Cham. https://doi.org/10.1007/978-3-030-72582-2_24
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