Aleia, 75008 Paris, France.
Institut Langevin, ESPCI Paris, PSL University, CNRS, Sorbonne Université, 75005 Paris, France.
Sensors (Basel). 2023 Jul 4;23(13):6142. doi: 10.3390/s23136142.
In the past few years, data privacy legislation has hampered the ability of WiFi network operators to count and map client activity for commercial and security purposes. Indeed, since client device MAC devices are now randomized at each transmission, aggregating client activity using management frames such as Probe Requests, as has been common practice in the past, becomes problematic. Recently, researchers have demonstrated that, statistically, client counts are roughly proportional to raw Probe Request counts, thus somewhat alleviating the client counting problem, even if, in most cases, ground truth measurements from alternate sensors such as cameras are necessary to establish this proportionality. Nevertheless, localizing randomized MAC clients at a network site is currently an unsolved problem. In this work, we propose a set of nine tools for extending the proportionality between client counts and Probe Requests to the mapping of client densities in real-world outdoor WiFi networks without the need for ground truth measurements. The purpose of the proposed toolkit is to transform raw, randomized MAC Probe Request counts into a density map calibrated to an estimated number of clients at each position.
在过去的几年中，数据隐私法规阻碍了 Wi-Fi 网络运营商为商业和安全目的而对客户端活动进行计数和绘制地图的能力。实际上，由于客户端设备的 MAC 地址现在在每次传输时都是随机的，因此使用管理帧（如 Probe Request）聚合客户端活动，如过去常见的做法，就会出现问题。最近，研究人员已经证明，从统计学上讲，客户端计数与原始 Probe Request 计数大致成比例，因此即使在大多数情况下，仍需要来自其他传感器（如摄像头）的真实测量值来确定这种比例关系，也可以在一定程度上缓解客户端计数问题。然而，在网络站点上定位随机化的 MAC 客户端仍然是一个未解决的问题。在这项工作中，我们提出了一套九种工具，用于将客户端计数和 Probe Request 之间的比例关系扩展到现实世界户外 Wi-Fi 网络中的客户端密度映射，而无需进行真实测量。所提出的工具包的目的是将原始的、随机化的 MAC Probe Request 计数转换为密度图，该密度图经过校准，以估计每个位置的客户端数量。
