An Effective Machine Learning Based Algorithm for Inferring User Activities From IoT Device Events

• Published in: IEEE journal on selected areas in communications Vol. 40; no. 9; pp. 2733 - 2745
• Main Authors: Xue, Guoliang, Wan, Yinxin, Lin, Xuanli, Xu, Kuai, Wang, Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Communications traffic; Internet of Things; IoT device events; Machine learning; Machine learning algorithms; Malfunctions; Pattern matching; Sensors; Smart buildings; Smart homes; Telecommunication traffic; Unsupervised learning; user activities
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2022.3191123

The rapid and ubiquitous deployment of Internet of Things (IoT) in smart homes has created unprecedented opportunities to automatically extract environmental knowledge, awareness, and intelligence. Many existing studies have adopted either machine learning approaches or deterministic approaches to infer IoT device events and/or user activities from network traffic in smart homes. In this paper, we study the problem of inferring user activity patterns from a sequence of device events by first deterministically extracting a small number of representative user activity patterns from the sequence of device events, then applying unsupervised learning to compute an optimal subset of these user activity patterns to infer user activity patterns. Based on extensive experiments with sequences of device events triggered by 2,959 real user activities and up to 30,000 synthetic user activities, we demonstrate that our scheme is resilient to device malfunctions and transient failures/delays, and outperforms the state-of-the-art solution.