Automatic Parameter Adjustment for Hybrid WiFi and BLE-based Congestion Measurement

The recent COVID-19 pandemic has made it extremely important to avoid crowded environments. In light of this, we are developing a congestion measurement system utilizing IoT technology, in which the congestion state of a given location is estimated by counting the number of WiFi Probe Request messages and BLE exposure notification messages sent from smartphones and laptops. Our congestion measurement system, however, suffers from an unstable measurement problem because the number of WiFi and BLE messages is highly dependent on the environment in which our system is deployed. This paper presents a congestion measurement system employing an automatic parameter adjuster that accurately estimates the number of people in various target locations. The parameters are automatically adjusted based on the seating capacity and area size of the target environment, as well as information gathered from a sensor which is designed to collect and analyze WiFi and BLE messages collected in a university cafeteria.