VIEW JOURNAL & SUBMIT

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Guest Editors

• Mengchu Zhou, New Jersey Institute of Technology, USA. Email: zhou@njit.edu

• Yixiong Feng, Guizhou University, China

• Qinglin Zhao, Macau University of Science and Technology, China

• Maria Pia Fanti, Polytechnic of Bari, Italy

 

Important Dates

• Submission: June 15, 2024

• Revision: Aug 1, 2024

• Final: Oct. 1, 2024

• Publication: Jan 2025

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Industry 4.0 focuses on the development and application of intelligent information technology in production process, emphasizing the intelligence and efficiency of the production process. It is based on the rapid development of Internet of Things and Big Data Analytics. Industry 5.0, as a continuation and improvement of Industry 4.0, emphasizes the in-depth cooperation and synergy between humans and machines in the production system, changes the focus from "machines" to "humans", and seeks to combine human creativity, flexibility and judgment with the efficient performance of machines to achieve a symbiotic relationship in the work. It is based on not only Internet of Things but also Internet of Behaviors together with AI and Big Data Analytics. The connotation of Industry 5.0 mainly includes “Human-Centric”, “Sustainability” and “Resilience”, with the aim of establishing the dominant and decision-making position of humans in a production system, and realizing human-centric sustainable and highly reliable industrial production.

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Internet of Things (IoT) provides the technological basis for information exchange between humans and devices by embedding sensors, actuators, and communication modules into devices to realize interconnection, data exchange, and remote control between devices. Internet of Behaviors (IoB) further strengthens the focus on worker behaviors and Man-Machine-Material-Method-Environment interactions in the manufacturing process, incorporating social factors into the IoT system and enhancing the intelligence and adaptability of the system.

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Under the framework of Industry 5.0, through the combination of IoT and IoB, humans and machines, and machines and machines can work together more intelligently and efficiently, realizing the close coupling of human beings, information processing systems, and physical processes. The interconnection of humans, machines and things at anytime and anywhere provides in-depth insights into the operating status of equipment and worker behaviors during the production process, thus providing more intelligent and personalized solutions for Industry 5.0.

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At present, the concept of Industry 5.0 and the related technologies of human-centric, sustainability and resilience have gradually become a research hotspot, while its theoretical research is still in the exploration and improvement stage. In addition, how to integrate the industrial IoT and IoB into the process of promoting Industry 5.0, so as to better realize more reliable and secure human-machine collaboration, information transmission and production decision-making, also needs to be further explored. In this regard, this special issue focuses on the new development and trend of basic theories and key technologies related to safe and reliable industrial IoT, IoB and Industry 5.0, and exploring the application prospects of IoT and IoB in the fields related to Industry 5.0 to advance industrial production. Experts and scholars engaged in theoretical and technological research in this field are welcomed to contribute.

 

Topics include but are not limited to

• Emerging methods, paradigms and architecture in IoT and IoB for Industry 5.0

• Advanced unified modeling techniques for Industry 5.0

• Cloud/Edge Computing-based Collaborative Scheduling Methods

• IoT/IoB-enabled efficient human-robot interaction and collaboration

• Intelligent perception methods, architectures and platforms in Industry 5.0

• Big data analytics and machine learning techniques for intelligent manufacturing

• IoT/IoB-driven collective decision-making and intelligent control

• Human factors and ergonomics for Industry 5.0

• Human-machine inter-cognitive methods

• Federated learning and blockchain technology for Industry 5.0

• Behavioral prediction, production process management via IoT/IoB

• Technology and application of human digital twin modelling for Industry 5.0

• Collaborative design and optimization in Industry 5.0

• IoT/IoB-enabled flexible Job shop scheduling in Industry 5.0

• Cloud computing, fog computing, and edge computing for Industry 5.0

• Multi-objective optimization methods for Industry 5.0

• Supply chain for Industry 5.0

• IoT/IoB-driven manufacturing, assembly, disassembly, and remanufacturing

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Bio-sketches of the Guest Editors

MengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, Beijing, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY in 1990.  He joined the Department of Electrical and Computer Engineering, New Jersey Institute of Technology in 1990, and is now a Distinguished Professor. His interests are in intelligent automation, robotics, Petri nets, Internet of Things, edge/cloud computing, and big data analytics.  He has over 1200 publications including 17 books, over 800 journal papers including over 650 IEEE Transactions papers, 31 patents and 32 book-chapters. He is a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man, and Cybernetics 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).

Yixiong Feng received his B.S. and M.S. degrees in Mechanical Engineering from Yanshan University, Qinhuang-dao, China in 1997 and 2000, respectively, and the Ph.D. degree in Mechanical Engineering from Zhejiang University, Hangzhou, China in 2004. He is currently a Professor with the State Key Laboratory of Public Big Data, Guizhou University, where he is also the deputy director of the State Key Laboratory of Fluid Power and Mechatronic Systems,  Zhejiang University. He has been a Principal Investigator of four projects supported by the National Natural Science Foundation of China and a project supported by the National High Tech Research and Development Program of China (863 Program). He has authored or coauthored more than 100 journals and conference proceeding papers on the topics. His current research interests include mechanical product design theory, intelligent automation, and advance manufacture technology. He is a recipient of the Zhejiang Province Outstanding Youth Fund and National Outstanding Youth Fund, and is now a Changjiang Scholar Distinguished Professor of the Ministry of Education.

Qinglin Zhao received his B.S. degree in mathematics education from the Hubei University, Wuhan, China, in 1998, M.S. degree in applied mathematics from the Huazhong University of Science and Technology, Wuhan, in 2001, and Ph.D. degree in computer architecture from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, in 2005. From May 2005 to August 2009, he worked as a Postdoctoral Researcher with The Chinese University of Hong Kong and the Hong Kong University of Science and Technology. Since September 2009, he has been with the School of Computer Science and Engineering, Macau University of Science and Technology, where he is currently a professor. He has published over 80 peer-reviewed papers, including over 20 IEEE Transactions papers, held over 30 patents, including 8 U.S. patents. His research interests include machine learning, blockchain and decentralization computing, Internet of Things, wireless communications and networking, cloud/fog computing, and quantum computing and quantum machine learning. He received the BOC Excellent Research Award of Macau University of Science and Technology in 2011 and 2015. He serves as an associate editor of IEEE Transactions on Mobile Computing and IET Communications.

 

Maria Pia Fanti received the Laurea degree in electronic engineering from the University of Pisa, Pisa, Italy, in 1983. She was a visiting researcher at the Rensselaer Polytechnic Institute of Troy, New York, in 1999. Since 1983, she has been with the Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy, where she is currently a Full Professor of system and control engineering and Chair of the Laboratory of Automation and Control. Her research interests include management and modeling of complex systems, such as transportation, logistics and manufacturing systems; discrete event systems; Petri nets; consensus protocols; fault detection. Prof. Fanti has published more than 345 papers and two textbooks on her research topics. Prof.  Fanti received from the IEEE SMCS in 2008 the award for best Associate editor of the Trans. on SMC and the award for the best technical committee chair.

She was senior editor of the IEEE Trans. on Automation Science and Engineering, and she is Associate Editor of the IEEE Trans. on Systems, Man, and Cybernetics: Systems. She was member at large of the Board of Governors of the IEEE Systems, Man, and Cybernetics Society, and currently she is member of the AdCom of the IEEE Robotics and Automaton Society, founder and chair of the Technical Committee on Automation in Logistics of the IEEE Robotics and Automation Society. Prof. Fanti was General Chair of the 2011 IEEE Conference on Automation Science and Engineering, the 2017 IEEE International Conference on Service Operations and logistics, and Informatics and the 2019 IEEE Systems, Man, and Cybernetics Conference. She is Fellow of IEEE.