Poster Abstract: Robust Deep Learning Based Residential Occupancy Detection With WiFi

In-house occupancy detection is vital for smart energy management, resource optimization, and home security. Traditional sensor-based solutions can be inaccurate and invasive. This paper presents a WiFi-based occupancy detection system that utilizes existing WiFi infrastructure and IoT devices. Our method employs a neural network with a shared CNN and a transformer block. Our preliminary evaluation, conducted using 18 unique IoT devices and data collected from 7 different homes over 42 days, demonstrates a detection accuracy of 94.88% with 8.12% false alarms in familiar environments, and 90.79% accuracy with 10.11% false alarms in new settings, significantly outperforming model-based methods.