WiFi-MAC address de-randomization based on Artificial Intelligence for people counting applications
Channel: Francisco Carlos Calderon
Date: 2022-03-29

The real-time sensing of the person number in a closed area allows generating commercial policies, managing resources, or verifying health conditions. Along the time, several solutions have been proposed to face this counting, from the use of mechanical systems (i.e., turnstiles) to the use of video cameras and artificial intelligence techniques. A common low-cost approach is counting the mobile phones present (assuming a direct correlation between them, e.g., one device for person) in a closed area using the WiFi-MAC address. Recently, some WiFi devices hide their WiFi-MAC to ensure their privacy, making it significantly harder to track and count them. In this paper, a WiFi-MAC address de-randomization technique was proposed, which is based on pre-filtering of WiFi-MAC addresses, devices indoor positioning using RSSI measurements, and cluster analysis. The proposed technique was experimentally tested with a mean error of one device in semi-controlled scenarios. They are closed areas with a known number of  WiFi devices inside the area, and an aleatory number of WiFi devices outside the area.