Infrastructure-Free Indoor Occupancy Estimation via Passive BLE Scanning

Accurately estimating indoor occupancy is fundamental to the development of modern smart buildings, which aim to optimize critical parameters such as Heating, Ventilation, and Air Conditioning (HVAC) control, safety, and resource management in real time to reduce energy waste. Traditional sensing approaches, including cameras, Passive Infrared (PIR) sensors, and CO 2 monitors, often encounter high deployment costs, maintenance overhead, and significant privacy concerns, particularly under General Data Protection Regulation (GDPR) regulations. This paper presents the design, implementation, and evaluation of a non-invasive occupancy estimation system that exclusively relies on Bluetooth Low Energy (BLE) scans performed via a mobile device, eliminating the need for prior structural information or dedicated sensing infrastructure. The proposed method analyzes statistical differences between various university environments, such as Laboratories, Classrooms, and Corridors, while integrating variables such as the number of fixed and mobile devices, the average device density per person, and the interference caused by signal bleed-through between adjacent rooms. Based on a foundational study of device ownership behavior, we develop context-dependent calibration coefficients to address the multi-device phenomenon, in which a single occupant may carry multiple Bluetooth Low Energy emitters. Our system utilizes a three-layer architecture that includes Passive Bluetooth scanning, Signal filtering with night-baseline infrastructure detection, and Automatic room-type classification. This design allows for the dynamic selection of estimation parameters without the need for manual input. Field experiments conducted across various university spaces over a multi-week data collection period demonstrate that our context-aware model significantly reduces estimation error compared to traditional device-counting methods. This approach offers a scal-able, cost-effective, and privacy-preserving engineering solution for real-time occupancy monitoring in smart campus environments.