Accurate occupancy estimation with WiFi and bluetooth/BLE packet capture

The capillary spread of personal devices equipped with wireless communication capabilities has enabled a series of high level services which build on capturing and processing the data packets emitted by such devices. In this paper we tackle the problem of exploiting such a methodology to perform occupancy estimation, i.e., understanding how many people are present in a specific place, an information which is valuable in many scenarios (HVAC and lighting system control, building energy optimisation, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely either on environmental sensors (CO2, temperature, humidity) or video cameras, which both have drawbacks: the former have generally low accuracy, while the latter require high setup and maintenance costs. In this paper we depart from such traditional approaches and propose a cheap and accurate occupancy estimation system based on the capture of both Wi-Fi and Bluetooth or Bluetooth Low Energy management frames transmitted from users’ devices. The system, implemented on low-cost hardware, leverages a supervised learning model to adapt to different spaces and transmits estimated occupancy information to a web-based dashboard. Experimental results in both indoor and outdoor uncontrolled scenarios demonstrate the validity of the proposed solution.