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Resilient Cyber-Physical Systems
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Resilient Cyber-Physical Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 14283

Special Issue Editors

Dr. Elvira Nica

E-Mail Website
Guest Editor
Bucharest University of Economic Studies, Romania
Interests: smart sustainable data-driven manufacturing; cyber-physical connected environments; Industrial Internet of Things systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Miloš Poliak

E-Mail Website
Guest Editor
Faculty of Operation and Economics of Transport and Communications, University of Žilina, Univerzitná 1, 010 06 Žilina, Slovakia
Interests: social law; sustainable development of urban transport; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. Vladimír Konečný

E-Mail Website
Guest Editor
University of Žilina, 8215/1 010 26 Žilina, Slovakia
Interests: networks and intelligent automation systems; big data analytics processes; machine learning techniques

Special Issue Information

Dear Colleagues,

Cyber–physical systems link the physical realm, mainly by use of sensors or actuators, with the virtual one of data processing, integrating computation, networking, and physical operations. Cyber–physical systems typically harness distributed sensor networks to collect, handle, and exchange data collectively, configuring cutting-edge kinds of autonomous systems. Actualized by multifarious battery-powered devices, cyber–physical systems connect physical entities with integrated computational facilities and information depositories.

Topics of interest for publication in this Special Issue include, but are not limited to, the following:

  • Big data for cyber–physical systems
  • Smart manufacturing based on cyber–physical systems
  • Security problems in cyber–physical systems
  • Cloud-based medical cyber–physical systems
  • Cyber–physical systems for performance monitoring
  • The management of resilient cyber–physical systems
  • Knowledge-based cyber–physical systems
  • Cyber–physical manufacturing control systems
  • Automotive cyber–physical systems

Dr. Elvira Nica
Dr. Miloš Poliak
Dr. Vladimír Konečný
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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 production systems
  • big data-driven smart manufacturing
  • sensing and computing technologies
  • autonomous decision-making algorithms

Published Papers (3 papers)

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Research

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15 pages, 2513 KiB  
Article
Secure and Sustainable Predictive Framework for IoT-Based Multimedia Services Using Machine Learning
by Naveed Islam, Majid Altamimi, Khalid Haseeb and Mohammad Siraj
Sustainability 2021, 13(23), 13128; https://0-doi-org.brum.beds.ac.uk/10.3390/su132313128 - 26 Nov 2021
Cited by 11 | Viewed by 1770
Abstract
In modern years, the Internet of Things (IoT) has gained tremendous growth and development in various sectors because of its scalability, self-configuring, and heterogeneous factors. It performs a vital role in improving multimedia communication and reducing production costs. The multimedia data consist of [...] Read more.
In modern years, the Internet of Things (IoT) has gained tremendous growth and development in various sectors because of its scalability, self-configuring, and heterogeneous factors. It performs a vital role in improving multimedia communication and reducing production costs. The multimedia data consist of various types and formats (text, audio, videos, etc.), which are forwarded in the form of blocks of bits in the network layer of TCP/IP. Due to limited resources available to IoT-built devices, most of the Multimedia Internet of Things (MIoT)-based applications are delay constraints, especially for big data content. Similarly, multimedia-based applications are more vulnerable to security burdens and lower the trust of data processing. In this paper, we present a secure and sustainable prediction framework for MIoT data transmission using machine learning, which aims to offer intelligent behavior of the system with information protection. Firstly, the network edges exploit a regression analysis for a real-time multimedia routing scheme and achieve precise delivery towards the media servers. Secondly, an efficient and low-processing asymmetric process is proposed to provide secure data transmission between the IoT devices, edges, and data servers. Extensive experiments are performed over the OMNET++ network simulator, and its significance is achieved by an average for energy consumption by 71%, throughput by 30.5%, latency by 22%, bandwidth by 34.5%, packets overheads by 38.5%, computation time by 12.5%, and packet drop ratio by 35% in the comparison of existing schemes. Full article
(This article belongs to the Special Issue Resilient Cyber-Physical Systems)
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Review

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23 pages, 729 KiB  
Review
Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review
by Mihai Andronie, George Lăzăroiu, Roxana Ștefănescu, Cristian Uță and Irina Dijmărescu
Sustainability 2021, 13(10), 5495; https://0-doi-org.brum.beds.ac.uk/10.3390/su13105495 - 14 May 2021
Cited by 124 | Viewed by 5379
Abstract
With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly [...] Read more.
With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing. Full article
(This article belongs to the Special Issue Resilient Cyber-Physical Systems)
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15 pages, 275 KiB  
Review
Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review
by Mihai Andronie, George Lăzăroiu, Mariana Iatagan, Iulian Hurloiu and Irina Dijmărescu
Sustainability 2021, 13(2), 751; https://0-doi-org.brum.beds.ac.uk/10.3390/su13020751 - 14 Jan 2021
Cited by 151 | Viewed by 6414
Abstract
In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems [...] Read more.
In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems constitute the components of data-driven sustainable smart manufacturing. Throughout September 2020, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable industrial value creation”, “cyber-physical production systems”, “sustainable smart manufacturing”, “smart economy”, “industrial big data analytics”, “sustainable Internet of Things”, and “sustainable Industry 4.0”. As we inspected research published only in 2019 and 2020, only 323 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 119, generally empirical, sources. Future research should investigate whether Industry 4.0-based manufacturing technologies can ensure the sustainability of big data-driven production systems by use of Internet of Things sensing networks and deep learning-assisted smart process planning. Full article
(This article belongs to the Special Issue Resilient Cyber-Physical Systems)
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