Developments in Cyber-Physical Systems and Cyber-Physical-Human Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 3014

Special Issue Editors


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Guest Editor
School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide 5005, Australia
Interests: cybersecurity; cyber–physical systems; autonomous vehicles; intelligent unmanned systems

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Guest Editor
College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Interests: networked control systems; event-triggered control; multi-agent systems; decision making; deep learning (artificial intelligence)

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Guest Editor
School of Electrical and Mechanical Engineering, the University of Adelaide, Adelaide 5005, Australia
Interests: fuzzy systems; robotic systems; cyber–physical–human systems; autonomous vehicles; artificial general intelligence

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Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: advanced system science and technology to improve industrial safety and human health; safety monitoring; fault detection; fault diagnosis; attack detection of industrial cyber-physical systems
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Special Issue Information

Dear Colleagues,

With the development of technology applied in computers, control theory and communication networks, cyber–physical systems (CPS) have been broadly studied, becoming the backbone of "Industry 4.0". A commonly accepted definition of CPS is that CPS is a system where software and hardware components are seamlessly integrated toward performing well-defined tasks. Unlike conventional control systems, CPSs integrate the cyber layer with the physical layer tightly and have a wide range of applications, such as aerospace systems, healthcare devices, smart grids, smart cities, and intelligent transportation. While defining the scope of "Industry 5.0", researchers deem that rapid developments in information, communication and collaborative technologies allow humans to take on more critical roles in CPS and achieve human-on-the-loop rather than the conventional approach of only including human-in-the-loop for some fundamental operations.  This innovation requires huge, complex human–machine collaboration never before encountered. CPSs must learn and adapt to the changing complex environments and adjust intelligently to collaborate with humans. Such complex systems with distinguishing characteristics of connectivity, diversity, autonomy, and scalability need to be modelled as ground-breaking cyber–physical–human systems (CPH).

This Special Issue aims to cover a wide range of emerging research on the foundational theoretic analysis and design of CPS and CPH. Prospective authors are cordially invited to submit their original manuscripts on topics including, but not limited to, the following:

  • Analysis and design of cyber-attacks;
  • Attack detection and prevention in CPS;
  • Privacy concerns within CPS;
  • Secure estimation and control for CPS;
  • Security issues of industrial CPS and its applications;
  • Trustworthy and reliable communications;
  • Threat and vulnerability analysis for CPS;
  • AI techniques in CPS security;
  • Human-on-the-loop control systems;
  • Human–machine collaboration;
  • Cyber–physical–human systems;
  • Real-world applications of CPS.

Dr. Zhi Lian
Prof. Dr. Mouquan Shen
Dr. Xin Yuan
Dr. Yuchen Jiang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • analysis and design of cyber-attacks
  • attack detection and prevention in CPS
  • privacy concerns within CPS
  • secure estimation and control for CPS
  • security issues of industrial CPS and its applications
  • trustworthy and reliable communications
  • threat and vulnerability analysis for CPS
  • AI techniques in CPS security

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Published Papers (2 papers)

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Research

11 pages, 677 KiB  
Article
Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite
by Fabrizio Maria Aymone and Danilo Pietro Pau
Information 2024, 15(11), 674; https://doi.org/10.3390/info15110674 - 28 Oct 2024
Viewed by 1336
Abstract
This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with [...] Read more.
This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with the centralized cloud processing. Emerging intelligent sensors equipped with computing assets to run neural network inferences and embedded in the same package, which hosts the sensing elements, present new challenges due to their limited memory resources and computational skills. This benchmark evaluates models trained with Quantization Aware Training (QAT) and compares their performance with Post-Training Quantization (PTQ) across three use cases: Human Activity Recognition (HAR) by means of the SHL dataset, Physical Activity Monitoring (PAM) by means of the PAMAP2 dataset, and superficial electromyography (sEMG) regression with the NINAPRO DB8 dataset. The results demonstrate the effectiveness of QAT over PTQ in most scenarios, highlighting the potential for deploying advanced AI models on highly resource-constrained sensors. The INT8 versions of the models always outperformed their FP32, regarding memory and latency reductions, except for the activations for CNN. The CNN model exhibited reduced memory usage and latency with respect to its Dense counterpart, allowing it to meet the stringent 8KiB data RAM and 32 KiB program RAM limits of the ISPU. The TCN model proved to be too large to fit within the memory constraints of the ISPU, primarily due to its greater capacity in terms of number of parameters, designed for processing more complex signals like EMG. This benchmark aims to guide the development of efficient AI solutions for In-Sensor Machine Learning Computing, fostering innovation in the field of Edge AI benchmarking, such as the one conducted by the MLCommons-Tiny working group. Full article
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31 pages, 628 KiB  
Article
A Dynamic Event-Triggered Secure Monitoring and Control for a Class of Discrete-Time Markovian Jump Systems: A Plug-and-Play Architecture
by Yi Gao, Yunji Li, Ziyan Hua, Junjie Chen and Yajun Wu
Information 2024, 15(10), 649; https://doi.org/10.3390/info15100649 - 17 Oct 2024
Viewed by 864
Abstract
In modern industrial applications, production quality, system performance, process reliability, and safety have received considerable attention. This article proposes a dynamic event-triggered attack estimator for Markovian jump stochastic systems susceptible to actuator deception attacks. Utilizing the developed estimator, the presented attack-tolerant control strategy [...] Read more.
In modern industrial applications, production quality, system performance, process reliability, and safety have received considerable attention. This article proposes a dynamic event-triggered attack estimator for Markovian jump stochastic systems susceptible to actuator deception attacks. Utilizing the developed estimator, the presented attack-tolerant control strategy can tolerate the effects of such attacks and ensure the mean-square convergence of the overall closed-loop system. A dynamic event-triggered mechanism is implemented on the sensor side to optimize communication efficiency. To address the potential threat of deception attacks, a plug-and-play (PnP) secure monitoring and control architecture is introduced. This architecture facilitates the seamless integration of the designed attack-tolerant controller with the nominal feedback controller, thereby enhancing system security without requiring significant modifications to the existing control structure. The practicality and effectiveness of the proposed approaches are demonstrated through experimental results on a switched boost converter circuit. Full article
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