Inferring User Activities from Internet of Things (IoT) Connected Device Events Using Machine Learning Based Algorithms

Dec 5, 2024· Guoliang Xue , Yinxin Wan , Xuanli Lin , Kuai Xu , Feng Wang · 2 min read
Summary
This published patent application describes a learning framework for inferring user activities from IoT device events. It extracts representative activity patterns and trains a model using a compact subset of event sequences that meets an accuracy threshold. The model then predicts activities from newly observed sequences.
Type
Publication
U.S. Patent Application Publication US20240403648A1
publication

Abstract

An Internet-of-Things (IoT) learning framework (IoT learning framework) may train AI models to infer user activities from IoT connected device events using machine learning based algorithms. According to such an example, processing circuitry obtains a training dataset indicating IoT device events and extracts representative user activity patterns from the sequences of IoT device events. In such an example, processing circuitry trains the AI model to learn an optimal subset of the sequences of IoT device events corresponding to a smallest quantity of the sequences of IoT device events to predict user activities with accuracy that satisfies a threshold and outputs the AI model. According to such an example, processing circuitry may obtain new data indicating new sequences of IoT device events and generates output indicating one or more user activities predicted by the AI model to have occurred based on the new sequences of IoT device events.

Guoliang Xue, Yinxin Wan, Xuanli Lin, Kuai Xu, and Feng Wang. “Inferring User Activities from Internet of Things (IoT) Connected Device Events Using Machine Learning Based Algorithms.” U.S. Patent Application Publication US20240403648A1, published December 5, 2024.

This record is a published patent application. Application 18/734,240 was filed June 5, 2024, with a provisional application 63/506,316 filed June 5, 2023. The assignee listed on the publication is the Arizona Board of Regents on Behalf of Arizona State University.

Internet of Things User Activity Inference Machine Learning
Guoliang Xue
Authors
Guoliang Xue
Professor of Computer Science and Engineering
Guoliang Xue is a professor in Arizona State University’s School of Computing and Augmented Intelligence and an IEEE Fellow. He investigates wireless and quantum networks, network security and privacy, and optimization. He earned his PhD in computer science from the University of Minnesota in 1991. His honors include the IEEE Communications Society’s 2019 William R. Bennett Prize, and he chaired the IEEE INFOCOM Steering Committee from 2020 through 2025.
Yinxin Wan
Authors
Yinxin Wan
Assistant Professor of Computer Science
Yinxin Wan is an assistant professor in the Department of Computer Science at the University of Massachusetts Boston. His research focuses on cybersecurity, secure and trustworthy artificial intelligence, network measurement, the Internet of Things, and quantum networking. He received his PhD in Computer Science from Arizona State University in 2023, advised by Guoliang Xue and Kuai Xu, and his bachelor’s degree in Information Security from the University of Science and Technology of China.
Xuanli Lin
Authors
Xuanli Lin
PhD Student in Computer Science

Xuanli Lin is a fifth-year PhD student in the Computer Science department at Arizona State University, supervised by Dr. Guoliang Xue.

His research interests include network optimization, network security, artificial intelligence, and the Internet of Things.

Kuai Xu
Authors
Kuai Xu
Professor of Computer Science
Kuai Xu is a professor of computer science in the School of Mathematical and Natural Sciences at Arizona State University. His research covers network security, network measurement and analysis, cloud computing, home networks, and online social networks. He received his PhD in Computer Science from the University of Minnesota in 2006 and his bachelor’s and master’s degrees in Computer Science from Peking University in 1998 and 2001, respectively.
Feng Wang
Authors
Feng Wang
Professor of Applied Computing
Feng Wang is a professor in the applied computing program in the School of Mathematical and Natural Sciences at Arizona State University. Her research spans network science, social media analysis, network optimization, network security, and wireless sensor networks. She received her PhD in Computer Science from the University of Minnesota, Twin Cities, in 2005, her master’s degree in Computer Science from Peking University in 1999, and her bachelor’s degree in Computer Science from Wuhan University in 1996. She joined Arizona State University in 2007.