Online Nonparametric Process Monitoring for IoT Systems Using Edge Computing

Abstract

Facilitated by modern sensing and integrated circuit technology, Internet of Things (IoT) systems now have the ability to intelligently and automatically collect data, perform computational tasks on edge, and efficiently make decisions locally or remotely. However, existing multivariate statistical process control (MSPC) methods commonly assume that sensors directly transmit the raw measurements to the central server, and thus the edge computing capability of the IoT systems is not effectively harnessed. To fill this literature gap, we propose a monitoring scheme tailored for IoT systems by leveraging their edge computational power. In particular, inspired by the inherent hierarchical structure of the IoT system, we construct the monitoring statistics hierarchically. At each edge device, we first construct a nonparametric stream-level statistic for each data stream and aggregate them to derive the device-level statistics. To eliminate the inconsistency in device-level statistics across different edge devices, the device-level statistics are transformed into time-related statistics, which are then sent to the central server. At the central server, a system-level monitoring statistic is constructed, and the most informative edge devices that should transmit statistics are intelligently determined. Theoretical analyses are carried out to justify the effectiveness of the data transmission strategy. Both simulations and a case study, which is based on an IoT system we built, are conducted to evaluate the performance and validate the superiority of the proposed monitoring scheme.

Keywords:

• edge device
• mean shift detection
• nonparametric control chart
• adaptive data transmission

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Additional information

Notes on contributors

Ziqian Zheng

Ziqian Zheng received a BS degree in automation engineering from Xi’an Jiaotong University, Xi’an, China, in 2019, and an MS degree in statistics from the University of Wisconsin, Madison, WI, USA, in 2023. Currently, he is a Ph.D. candidate in industrial engineering at the University of Wisconsin–Madison, Madison, WI, USA. His research focuses on developing advanced multimodal data analytics methodologies for effective inference, modeling, monitoring, and decision-making in complex systems.

Jiahui Zhang

Jiahui Zhang received the B.E. degree in industrial engineering from Tsinghua University, Beijing, China, in 2022. Currently, she is working toward the Ph.D. degree at the Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA. Her research interests are process monitoring, statistical modeling and data science. Ms. Zhang is a member of INFORMS.

Lingyun Xiao

Lingyun Xiao received a B.A. degree in applied mathematics and economics from University of California, Berkeley, CA, USA in 2020, and an M.S. degree in industrial and systems engineering from University of Wisconsin, Madison, WI, USA in 2022. He is currently pursuing a Ph.D. degree in operations research and industrial engineering from The University of Texas, Austin. His current research interests include artificial intelligence, reinforcement learning, multi-agent systems and robotics.

Warren R. Williams

Warren R Williams received a BS degree in mechanical engineering from Mississippi State University, Starkville, MS, USA, in 2020. Currently, he is a research mechanical engineer with US Army Corps of Engineers. His work is focused on developing edge computing solutions, specializing in hardware integration, data management, networking, and anomaly detection.

Jing-Ru C. Cheng

Jing-Ru C. Cheng received a PhD degree in computer science from Penn State University, University Park, PA, USA, in 2002. She has been a computer scientist with the U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA, since 2002. Her current research interests include data analytics, object detection, parallel algorithm development, software tool development for scientific computing, and multiscale multiphysics code development.

Kaibo Liu

Kaibo Liu received a BS degree in industrial engineering from the Hong Kong University of Science, and Technology in 2009, an MS degree in statistics and a PhD degree in industrial engineering from the Georgia Institute of Technology, in 2011 and 2013, respectively. Currently, he is a professor with the Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, USA. His research interests are data fusion for process modeling, monitoring, diagnosis, prognostics and decision making. Dr. Liu is a member of ASQ, SME, INFORMS, IEEE, and IISE.