Identifying Data-Flow Errors in Cyber-Physical Systems Based on the Simplified Merged Process of Petri Nets

Min Wang; Yike Wang; Xiao Chen; Lu Liu; MengChu Zhou; Xiaobing Sun; Shanchen Pang

doi:10.1109/JAS.2025.125549

Volume 12 Issue 10

Oct. 2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > 12(10): 2002-2014

M. Wang, Y. Wang, X. Chen, L. Liu, M. C. Zhou, X. Sun, and S. Pang, “Identifying data-flow errors in cyber-physical systems based on the simplified merged process of Petri nets,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2002–2014, Oct. 2025. doi: 10.1109/JAS.2025.125549

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M. Wang, Y. Wang, X. Chen, L. Liu, M. C. Zhou, X. Sun, and S. Pang, “Identifying data-flow errors in cyber-physical systems based on the simplified merged process of Petri nets,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2002–2014, Oct. 2025. doi: 10.1109/JAS.2025.125549

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Identifying Data-Flow Errors in Cyber-Physical Systems Based on the Simplified Merged Process of Petri Nets

doi: 10.1109/JAS.2025.125549

Funds: This work is partially supported by the National Natural Science Foundation of China (62402415), and in part by the Natural Science Foundation of Shandong Province of China (ZR2024MF129), and in part by State Key Laboratory of Massive Personalized Customization System and Technology (No. H & C-MPC-2023-02-03)

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Author Bio:
Min Wang received the Ph.D. degree in control science and engineering from the China University of Petroleum in 2022. She is currently a Lecturer and Master’s Supervisor with the Yangzhou University. Her research interests include formal methods,system anomaly detection and optimization, and theory and application of Petri net

Yike Wang received the B.S. degree from the School of Computer Science and Software Engineering, Southwest Petroleum University in 2023. He is a graduate student with the Yangzhou University. His current research interests include system anomaly detection and optimization, theory and application of Petri net

Xiao Chen holds MSc and Ph.D. degrees in computing science from Newcastle University, UK, and currently serves as a Lecturer in the School of Computing and Mathematical Sciences at the University of Leicester, UK. Previously, he was a Marie Skłodowska-Curie fellow at the School of Informatics at the University of Edinburgh, UK. His research interests revolve around the theory of distributed systems, blockchains, and consensus protocols, with a focus on blockchain-supported distributed AI systems. Additionally, he specialises in stochastic and formal modelling, as well as AI-driven optimisation for large-scale systems

Lu Liu received the MSc degree in data communication systems from Brunel University, UK, in 2003 and the Ph.D. degree from the University of Surrey, UK, in 2008. He is a Professor of Artificial Intelligence at the University of Exeter, UK. His research interests are in the areas of artificial intelligence, data science, sustainable systems and the Internet of Things. He is a Fellow of British Computer Society (BCS)

MengChu Zhou (Fellow, IEEE) received the B.S. degree in Control Engineering from Nanjing University of Science and Technology in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology in 1986, and Ph.D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990. He joined New Jersey Institute of Technology (NJIT), Newark, NJ, USA, in 1990, and has been a Distinguished Professor of Electrical and Computer Engineering since 2013. His research interests are in intelligent automation, AI, Petri nets, robotics, Internet of Things, big data analytics, cloud/edge computing, transportation and energy systems. He has over 1400 publications including 17 books, over 900 journal papers (over 700 in IEEE transactions), and 32 book-chapters. He holds 31 patents and several pending ones. His recently co-authored books include Learning Automata and their Applications to Intelligent Systems, IEEE Press/Wiley, Hoboken, NJ, 2024 (with J. Zhang), Device-Edge-Cloud Continuum Paradigms, Architectures and Applications, Springer Nature, 2023 (with C. Savaglio, G. Fortino, and J. Ma) and Sustainable Manufacturing Systems: An Energy Perspective, IEEE Press/Wiley, Hoboken, Hoboken, NJ, 2022 (with L. Li).He served as Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica, Associate Editor of IEEE Transactions on Robotics and Automation, IEEE Transactions on Automation Science and Engineering, and IEEE Transactions on Industrial Informatics, and Editor of IEEE Transactions on Automation Science and Engineering. He served as a Guest-Editor for many journals including IEEE Internet of Things Journal, IEEE Transactions on Industrial Electronics, IEEE Transactions on Evolutionary Computation, and IEEE Transactions on Semiconductor Manufacturing. He is presently Senior Editor of IEEE Transactions on Intelligent Transportation Systems, and Associate Editor of Research, IEEE Transactions on Industrial Informatics, IEEE Internet of Things Journal, and Frontiers of Information Technology & Electronic Engineering. He is founding Chair/Co-chair of Technical Committee on AI-based Smart Manufacturing Systems and Technical Committee on Humanized Crowd Computing of IEEE Systems, Man, and Cybernetics Society (SMC), Technical Committee on Semiconductor Manufacturing Automation of IEEE Robotics and Automation Society (RAS). He chaired IEEE North Jersey Section SMC Chapter during 2013−2023 and has been the Chair of the RAS Chapter since 2024. He is also a member of IEEE TAB Periodicals Committee and Periodicals Review and Advisory Committee. He served as Vice-Chair of Fellow Evaluation Committee and Vice-President for Conferences and Meetings of IEEE Systems, Man, and Cybernetics Society. He was General Chair of IEEE Conf. on Automation Science and Engineering, Washington D.C., August 23−26, 2008, General Co-Chair of 2003 IEEE International Conference on System, Man and Cybernetics (SMC), Washington DC, October 5−8, 2003, 2019 IEEE International Conference on SMC, Bari, Italy, Oct. 6−9, 2019, 2022 IEEE International Conference on SMC, Prague, Czech, October 9−12, 2022, and 2024 IEEE International Conference on SMC, Kuching, Malaysia, Oct. 6−10, 2024, Founding General Co-Chair of 2004 IEEE Int. Conf. on Networking, Sensing and Control, Taipei, March 21−23, 2004, and General Chair of 2006 IEEE Int. Conf. on Networking, Sensing and Control, Ft. Lauderdale, Florida, USA, April 23−25, 2006. He was Program Chair of 2010 IEEE International Conference on Mechatronics and Automation, August 4−7, 2010, Xi’an, China, 1998 and 2001 IEEE International Conference on SMC and 1997 IEEE International Conference on Emerging Technologies and Factory Automation. Dr. Zhou has led or participated in over 60 research and education projects with total budget over $14M, funded by the National Science Foundation, New Jersey Science and Technology Commission, and industry. He is a recipient of Excellence in Research Prize and Medal from NJIT, Humbt Research Award for US Senior Scientists from Alexander von Humbt Foundation, and Franklin V. Taylor Memorial Award, the Norbert Wiener Award, and Lotfi A. Zadeh Pioneer Award from IEEE SMC Society, Computer-Integrated Manufacturing UNIVERSITY-LEAD Award from Society of Manufacturing Engineers, Distinguished Service Award from IEEE Robotics and Automation Society, and Edison Patent Award from the Research & Development Council of New Jersey. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is Fellow of IEEE, International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), Chinese Association of Automation (CAA), and National Academy of Inventors (NAI)

Xiaobing Sun received the B.E. degree in computer science and technology from the Jiangsu University of Science and Technology, Zhenjiang, China, in 2007, and the Ph.D. degree in computer science and technology from the School of Computer Science and Engineering, Southeast University, Nanjing, China, in 2012. He is currently a Professor with the School of Information Engineering, Yangzhou University, Yangzhou, China. He has authored and coauthored more than 80 papers in refereed international journals and conferences, and has authored more than 20 patents. His research interests include software maintenance and evolution, software repository mining, and intelligence analysis

Shanchen Pang received the Ph.D. degree in computer software and theory from Tongji University in 2008. He is currently a Professor at China University of Petroleum (East China). His research interests include theory and application of Petri nets, service computing, trusted computing, and artificial intelligence
Corresponding author: MengChu Zhou, e-mail: zhou@njit.edu
Received Date: 2024-09-26
Revised Date: 2025-04-12
Accepted Date: 2025-05-27

Abstract

Abstract

Data-flow errors are prevalent in cyber-physical systems (CPS). Although various approaches based on business process modeling notation (BPMN) have been devised for CPS modeling, the absence of formal specifications complicates the verification of data-flow. Formal techniques such as Petri nets are popularly used for identifying data-flow errors. However, due to their interleaving semantics, they suffer from the state-space explosion problem. As an unfolding method for Petri nets, the merged process (MP) technique can well represent concurrency relationships and thus be used to address this issue. Yet generating MP is complex and incurs substantial overhead. By designing and applying α-deletion rules for Petri nets with data (PNDs), this work simplifies MP, thus resulting in simplified MP (SMP) that is then used to identify data-flow errors. Our approach involves converting a BPMN into a PND and then constructing its SMP. The algorithms are developed to identify data-flow errors, e.g., redundant-data and lost-data ones. The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS. It is expected to prevent the problems caused by data-flow errors, e.g., medical malpractice and economic loss in some practical CPS. Its practicality and efficiency of the proposed method through several CPS. Its significant advantages over the state of the art are demonstrated.
- Business process modelling notation (BPMN),
- cyber-physical system (CPS),
- data-flow errors,
- discrete event system,
- fault detection,
- model simplification,
- Petri net

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Identifying Data-Flow Errors in Cyber-Physical Systems Based on the Simplified Merged Process of Petri Nets

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