Networking Technologies for Cyber-Physical Systems

A special issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: 30 September 2024 | Viewed by 7868

Special Issue Editor

Department of Electrical Engineering, University of North Texas, Denton, TX, USA
Interests: signal processing; machine learning; sensor systems; wireless networks

Special Issue Information

Dear Colleagues,

Cyber-physical systems (CPSs) are engineered intelligent systems based on the seamless integration of networking, computing, and physical processes. Advances in CPSs will enable critical innovations for dramatically improved system performance (capability, usability, adaptability, scalability, resiliency, etc.) in a broad range of application domains, including agriculture, environment, healthcare, energy, manufacturing, transportation, and civil infrastructure, among many others. CPSs are fundamentally data-centric and network-based. Specifically, CPSs depend heavily on real-term data exchange between computational and physical components through the sensors and actuators embedded in physical processes, and on network-based data sharing among distributed sensing, processing, and control components as well as cloud computing resources.

As CPSs are being applied to an increasing number of diverse application domains, a wide range of networking technologies have been employed to meet different and often conflicting design requirements. With this Special Issue, we expect to motivate further research and development efforts in innovative CPS networking technologies, and to provide a unique opportunity to allow researchers from different domains to collaborate in research and share new research findings. We invite researchers to contribute original research articles focused on the state-of-the-art CPS networking technologies. The topics of interest include, but are not limited to, the following:

  • Design, simulation, implementation, and analysis of CPS networking technologies;
  • Networking technologies empowered by machine learning and artificial intelligence;
  • Network-enabled collaborative sensing, processing, and control;
  • Distributed real-time learning and decision for CPS networks;
  • Timing and synchronization for CPS networks;
  • Convergence of networking technologies for CPS and Internet of Things;
  • Convergence of sensor networks and CPSs;
  • Networking technologies for integration of edge computing in CPSs;
  • Experimental research, testbed development, and empirical performance studies;
  • In-depth surveys of the current state-of-the-art, challenges, and future directions.

Dr. Xinrong Li
Guest Editor

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. Network is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cyber-physical systems
  • Internet of Things
  • edge computing
  • sensor networks

Published Papers (2 papers)

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Research

22 pages, 1811 KiB  
Article
A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks
by Md Mamunur Rashid, Shahriar Usman Khan, Fariha Eusufzai, Md. Azharuddin Redwan, Saifur Rahman Sabuj and Mahmoud Elsharief
Network 2023, 3(1), 158-179; https://doi.org/10.3390/network3010008 - 30 Jan 2023
Cited by 24 | Viewed by 4519
Abstract
The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks [...] Read more.
The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method. Full article
(This article belongs to the Special Issue Networking Technologies for Cyber-Physical Systems)
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16 pages, 450 KiB  
Article
Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
by Dennis Krummacker, Benedikt Veith, Christoph Fischer and Hans Dieter Schotten
Network 2022, 2(3), 440-455; https://doi.org/10.3390/network2030027 - 23 Aug 2022
Cited by 3 | Viewed by 2726
Abstract
As 5G enters the application field of industrial communications, compatibility with technologies of wired deterministic communications such as Time-Sensitive Networking (TSN) needs to be considered during the standardization process. While consideration of underlying integration architectures and basic resource mapping are already part of [...] Read more.
As 5G enters the application field of industrial communications, compatibility with technologies of wired deterministic communications such as Time-Sensitive Networking (TSN) needs to be considered during the standardization process. While consideration of underlying integration architectures and basic resource mapping are already part of the standard, necessary traffic forwarding schemes are currently planned to be deployed in additional interfaces located at the edge of a 5G System. This analysis highlights the extent to which internal 5G mechanisms can be used to execute the traffic forwarding of TSN streams according to the requirements of the TSN control plane. It concludes with the recognition that a static prioritization of logical channels is not appropriate for the treatment of TSN streams over the 5G air interface. A novel prioritization mechanism of logical data channels is derived, which enables the execution of TSN-compliant traffic shaping over 5G RAN. Subsequently, a proof of concept is implemented and simulated for evaluation. Full article
(This article belongs to the Special Issue Networking Technologies for Cyber-Physical Systems)
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