Cyber-Physical Systems in Industrial IoT

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 6798

Special Issue Editors


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Guest Editor
Department of Information Technology, Old Dominion University, Norfolk, VA 23529, USA
Interests: industrial information integration engineering; industrial information systems; industrial informatics; enterprise systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science and Creative Technologies, University of the West of England, Coldharbour Ln, Bristol BS16 1QY, UK
Interests: cyber security; digital forensics; internet of things; network security

Special Issue Information

Dear Colleagues,

In general, a commonly accepted definition of cyber-physical systems (CPSs) refers to systems where software and hardware components are seamlessly integrated toward performing well-defined tasks.

CPSs are one of the core technologies of Industry 4.0. The integration of CPSs is essential in Industry 4.0 functioning. With CPSs, machines are able to communicate with each other, and decentralized control systems are able to optimize production. The integration of CPSs is leading to complexities emerging from the interactions among cyber systems and the uncertain dynamic behavior of physical systems. In recent years, CPSs have become increasingly interconnected and more deeply intertwined as they interact with each other in a myriad of ways. The integration of constituent systems in CPSs is a challenge. A suitable systems theory concerning the interconnection and interactions of cyber and physical systems as a whole is needed, and larger contexts need to be made explicit.

CPSs are increasingly interconnected with a meta-system such as the Industry 4.0 ecological environment, and their behavior depends on interactions with other systems components. Numerous interactions of different characteristics may be involved, such as interactions within a system or a subsystem, interactions between systems and/or subsystems, and interactions between a system and its environment. Such interrelated subsystems are capable of dynamic change. Interactions can occur between interactions as well, and some interactions only emerge when the system as a whole is considered. Industry 4.0 technologies are currently amplifying such interactions. If Industry 4.0 is to be employed successfully, these interactions must be understood and the methods for designing and implementing Industry 4.0 technologies must account for such interactions. Currently, these complex interactions are not being heavily investigated. One of the challenges we face is making use of cutting-edge ICT as well as systems science and engineering techniques to understand such complex interactions.

This Special Issue will focus on (but is not limited to) the following topics:

- New computing architecture for CPSs in industrial IoT;

- New communication mechanisms for CPSs in industrial IoT;

- Advanced AI/ML models for CPSs in industrial IoT;

- Advanced networking among CPSs for industrial IoT;

- Applications of CPSs in industrial IoT.

Prof. Dr. Li Da Xu
Dr. Shancang Li
Guest Editors

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Keywords

  • cyber-physical systems (CPSs)
  • IoT
  • industrial IoT

Published Papers (2 papers)

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Research

11 pages, 2249 KiB  
Article
Attention-Mechanism-Based Face Feature Extraction Model for WeChat Applet on Mobile Devices
by Jianyu Xiao, Hongyang Zhou, Qigong Lei, Huanhua Liu, Zunlong Xiao and Shenxi Huang
Electronics 2024, 13(1), 201; https://doi.org/10.3390/electronics13010201 - 2 Jan 2024
Viewed by 1246
Abstract
Face recognition technology has been widely used with the WeChat applet on mobile devices; however, facial images are captured on mobile devices and then transmitted to a server for feature extraction and recognition in most existing systems. There are significant security risks related [...] Read more.
Face recognition technology has been widely used with the WeChat applet on mobile devices; however, facial images are captured on mobile devices and then transmitted to a server for feature extraction and recognition in most existing systems. There are significant security risks related to personal information leakage with these transmissions. Therefore, we propose a face recognition framework for the WeChat applet in which face features are extracted in WeChat by the proposed Face Feature Extraction Model based on Attention Mechanism (FFEM-AM), and only the extracted features are transmitted to the server for recognition. In order to balance the prediction accuracy and model complexity, the structure of the proposed FFEM-AM is lightweight, and Efficient Channel Attention (ECA) was introduced to improve the prediction accuracy. The proposed FFEM-AM was evaluated using a self-built database and the WeChat applet on mobile devices. The experiments show that the prediction accuracy of the proposed FFEM-AM was 98.1%, the running time was less than 100 ms, and the memory cost was only 6.5 MB. Therefore, this demonstrates that the proposed FFEM-AM has high prediction accuracy and can also be deployed with the WeChat applet. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Industrial IoT)
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24 pages, 696 KiB  
Article
Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security
by Sarah Bin Hulayyil, Shancang Li and Lida Xu
Electronics 2023, 12(18), 3927; https://doi.org/10.3390/electronics12183927 - 18 Sep 2023
Cited by 3 | Viewed by 5005
Abstract
Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and [...] Read more.
Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in IoT environments, and a review of recent research trends is presented. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Industrial IoT)
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