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

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 i...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2022.3191123