Demonstrating Infrastructure-Free Indoor Occupancy Visualization using Passive BLE Sensing

Understanding how people occupy indoor spaces is important for applications such as smart building management, space utilization analysis, and adaptive environments. Traditional occupancy sensing solutions often rely on dedicated infrastructure, such as cameras, Wi-Fi access points, or specialized sensors, which may raise privacy concerns and require complex deployments. This work demonstrates an infrastructure-free approach for estimating indoor occupancy using passive Bluetooth Low Energy (BLE) scanning from commodity smartphones. A BLE scanner application for Android and iOS collects BLE advertisement packets emitted by nearby personal devices, which are treated as ambient signals that indirectly reflect human presence. To validate the system we collected a dataset within an academic building through repeated scanning sessions across two classrooms, a lab, and a university hall. The collected observations are aggregated by room and time and visualized through an interface that overlays device-density heat maps on building floor plans, enabling the exploration of spatial and temporal activity patterns. The proposed demonstration will be deployed at the AVI conference venue on the island of San Servolo, where smartphones in conference rooms will perform passive BLE scans and update the visualization interface, allowing participants to observe how device density evolves across rooms and time during the event.