Device-free occupancy detection refers to the identification and estimation of the presence, count, or location of people within a space using ambient wireless signals — such as Wi-Fi CSI or Bluetooth Low Energy — without requiring individuals to carry or wear any dedicated sensing device. This approach matters because it enables passive, unobtrusive monitoring for applications such as smart building energy management, security, and residential context awareness, without imposing any burden on occupants or relying on device ownership or compliance. Key variants include binary presence detection (occupied vs. unoccupied), room-level or zone-level localization, and fine-grained occupancy counting, with methods ranging from threshold-based signal analysis to machine learning and reinforcement learning approaches that adapt to dynamic environmental changes.
Source Papers
- BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection ↗ — BLE Can See: A Reinforcement Learning Approach for RF-based
- Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT ↗ — Device-free occupancy detection and crowd counting in smart
- Implementing Wi-Fi CSI-based room-level occupancy Estimation: an experimental study in multi-zone residential environments ↗ — Implementing Wi-Fi CSI-based room-level occupancy Estimation
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions ↗ — Occupancy Prediction in IoT-Enabled Smart Buildings: Technol