Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes

• Published in: IEEE internet of things journal Vol. 5; no. 5; pp. 3991 - 4002
• Main Authors: Yang, Jianfei, Zou, Han, Jiang, Hao, Xie, Lihua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Channel state information (CSI); Cloud computing; Commercial off-the-shelf technology; Cybersecurity; Detection; human activity recognition; Human behavior; Internet of Things; Machine learning; occupancy sensing; Performance evaluation; Privacy; Routers; Smart buildings; Smart houses; State (computer science); Wearable technology; WiFi; Wireless communication; Wireless fidelity; Wireless sensor networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2018.2849655

Intelligent occupancy sensing is becoming a vital underpinning for various emerging applications in smart homes, such as security surveillance and human behavior analysis. However, prevailing approaches mainly rely on video camera, ambient sensors, or wearable devices, which either requires arduous deployment or arouses privacy concerns. In this paper, we present a novel real-time, device-free, and privacy-preserving WiFi-enabled Internet of Things platform for occupancy sensing, which can promote a myriad of emerging applications. It is designed to achieve an optimal tradeoff between performance and scalability. Our system empowers commercial off-the-shelf WiFi routers to collect channel state information (CSI) measurements and provides an efficient cloud server for computing via a lightweight communication protocol. To demonstrate the usefulness of our platform, an occupancy detection system is developed by exploiting the CSI curve of human presence. Furthermore, we also design an innovative activity recognition system based on our platform and machine learning techniques with high availability and extensibility. In the evaluation, the experimental results show that our platform enables these applications efficiently, with the accuracy of 96.8% and 90.6% in terms of occupancy detection and recognition, respectively.