WiFi-enabled Occupancy Monitoring in Smart Buildings with a Self-Adaptive Mechanism

The building energy saving (BES) has been the subject of extensive research for reducing the energy consumption inside the buildings. One of the key solution for energy saving in buildings is to minimize the energy supply to the building areas that are not occupied by the inhabitants. However, this requires effective monitoring of occupants regardless of unpredictable variations in indoor environment, such as variation in the space size, furniture arrangement, nature of occupant's activity (e.g., varied intensities and instances) etc. Currently, various occupancy monitory solutions have been employed in the existing smart buildings, namely PIR sensors, CO2 sensors, cameras, etc. However, they are costly and sometimes not interoperable to the complex variations in indoor environments. In this paper, we leveraged the fine-grained information of physical layer (i.e., channel state information - CSI) of the commodity WiFi for occupancy detection and developed a self-adoptive method which is interoperable with complex variations in the indoor environment. In indoor contexts of different sized, varied intensities of physical activity, and various instances of activity of daily living (ADL), our testbed evaluation showed an average detection rate of 98.9%, 98.5%, and 98.1%, respectively.