Extracting Spatial Information of IoT Device Events for Smart Home Safety Monitoring

May 19, 2023· Yinxin Wan , Xuanli Lin , Kuai Xu , Feng Wang , Guoliang Xue · 1 min read
Summary
IoTDuet uses smart-home network traffic to determine whether an IoT device event was triggered locally or remotely. It examines cloud communication by controlling devices, including relatively stable domain-name information, to recover this spatial context. The paper explores how that information supports safety monitoring in smart homes.
Type
Publication
IEEE INFOCOM 2023 - IEEE Conference on Computer Communications, 1-10. IEEE
publication

Abstract

Smart home IoT devices have been widely deployed and connected to many home networks for various applications such as intelligent home automation, connected healthcare, and security surveillance. The network traffic traces generated by IoT devices have enabled recent research advances in smart home network measurement. However, due to the cloud-based communication model of smart home IoT devices and the lack of traffic data collected at the cloud end, little effort has been devoted to extracting the spatial information of IoT device events to determine where a device event is triggered. In this paper, we examine why extracting IoT device events’ spatial information is challenging by analyzing the communication model of the smart home IoT system. We propose a system named IoTDuet for determining whether a device event is triggered locally or remotely by utilizing the fact that the controlling devices such as smartphones and tablets always communicate with cloud servers with relatively stable domain name information when issuing commands from the home network. We further show the importance of extracting spatial information of IoT device events by exploring its applications in smart home safety monitoring.

Internet of Things User Activity Inference Device Events Spatial Information
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.
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.