MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence
This addresses the challenge of accurately tracking pedestrian and vehicle movements in urban environments for mobility intelligence, representing a strong domain-specific advancement.
The paper tackles the problem of WiFi-based mobility monitoring hindered by MAC address randomization, proposing MobRFFI, an AI framework for device re-identification using radio frequency fingerprinting, achieving up to 100% accuracy in single-day scenarios and improving from 41% to 100% in multi-day scenarios with multiple receivers.
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.