WiFi-based mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movements. However, MAC address randomization introduces a significant obstacle in accurately estimating congestion levels and path trajectories. To this end, we consider radio frequency fingerprinting and re-identification for attributing WiFi traffic to emitting devices without the use of MAC addresses. We present MobRFFI, an AI-based device fingerprinting and re-identification framework for WiFi networks that leverages an encoder deep learning model to extract unique features based on WiFi chipset hardware impairments. It is entirely independent of frame type. When evaluated on the WiFi fingerprinting dataset WiSig, our approach achieves 94% and 100% device accuracy in multi-day and single-day re-identification scenarios, respectively. We also collect a novel dataset, MobRFFI, for granular multi-receiver WiFi device fingerprinting evaluation. Using the dataset, we demonstrate that the combination of fingerprints from multiple receivers boosts re-identification performance from 81% to 100% on a single-day scenario and from 41% to 100% on a multi-day scenario.

翻译：在城市环境中，基于WiFi的移动监控可以为行人和车辆的移动轨迹提供有价值的洞察。然而，MAC地址随机化给准确估计拥堵程度和路径轨迹带来了重大障碍。为此，我们考虑采用射频指纹识别与重识别技术，在不使用MAC地址的情况下将WiFi流量归因于发射设备。我们提出了MobRFFI，一个基于人工智能的WiFi网络设备指纹识别与重识别框架。该框架利用编码器深度学习模型，基于WiFi芯片组的硬件损伤提取独特特征。它完全独立于帧类型。在WiFi指纹数据集WiSig上进行评估时，我们的方法在多日和单日重识别场景中分别实现了94%和100%的设备识别准确率。我们还收集了一个新颖的数据集MobRFFI，用于细粒度的多接收器WiFi设备指纹识别评估。利用该数据集，我们证明了组合多个接收器的指纹可以将单日场景下的重识别性能从81%提升至100%，将多日场景下的性能从41%提升至100%。