WiSOM: WiFi-enabled self-adaptive system for monitoring the occupancy in smart buildings

Abstract

There has been extensive research on building energy saving (BES), which aims to reduce energy consumption inside buildings. One of the key solutions for energy saving in buildings is to reduce energy consumption in areas that are not occupied by inhabitants. However, effective monitoring of occupants for energy-saving purposes can be challenging due to unpredictable variations in the indoor environment, such as variations in space size, furniture arrangement, the nature of occupants’ activities (e.g., varied intensities and instances), and penetration losses of walls. Unfortunately, the existing solutions for occupancy monitoring in smart buildings, such as PIR sensors, CO2 sensors, and cameras, etc., are expensive, require excessive maintenance, and are not adaptable to the complex variations in indoor environments. This paper introduces WiSOM, for occupancy detection that utilizes the channel state information (CSI), of commodity WiFi. The method is self-adaptive and designed to handle complex variations in indoor environments. We conducted a thorough analysis of WiSOM and evaluated it under various indoor conditions, including the impact of multipath effects, the detection of different intensities and instances of activities of daily living (ADL), and the impact of wall absorption in a real-home scenario. Our evaluation demonstrated an average detection rate of 98.25% for multipath effects, 96.5% and 98.1% for different intensities and instances of ADL, and 94.4% for wall absorption. Additionally, we assessed WiSOM’s resilience to temporal variation in the CSI and achieved a false alarm rate of less than 2%. In comparison to recent baselines, WiSOM outperformed, achieving up to a 21% improvement in detection rate within real-house scenarios.