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IIoT Enabling Next Generation Digital Factories
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IIoT Enabling Next Generation Digital Factories

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 13479

Special Issue Editors

Dr. Luca Bedogni

E-Mail Website
Guest Editor
Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, 41121 Modena MO, Italy
Interests: context-aware computing; vehicular networks; privacy; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Alexandros Bousdekis

E-Mail Website
Guest Editor
Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), 15780 Athens, Greece
Interests: industry 4.0; intelligent systems; management of information systems; predictive & prescriptive analytics
Dr. Moritz von Stietencron

E-Mail Website
Guest Editor
BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany
Interests: development and application of IoT systems and flexible Digital Twins

Special Issue Information

Dear Colleagues,

This special issue targets the recent advances that disruptive technologies such as the IoT and IIoT have in Industry 4.0 scenarios and Digital factories. The possibility of having unprecedented pervasiveness of devices and sensors in modern industrial machines enables novel and tailored services. Moreover, Machine Learning techniques and Artificial Intelligence allow to automate tasks by learning from past experiences, key in many scenarios. In this special issue we are then welcoming submissions related to:

  • Internet of Things
  • Industrial Internet of Things
  • Industry 4.0
  • Smart Manufacturing
  • Predictive Maintenance
  • Digital Factories
  • Big Data in Industry 4.0
  • Cloud to Edge architectures in Industry 4.0
  • Machine learning and Artificial Intelligence in Industry 4.0
  • Cyber Physical Systems
  • Digital Twins
  • Business Process Modeling

Dr. Luca Bedogni
Dr. Alexandros Bousdekis
Dr. Moritz von Stietencron
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. Sensors 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 2600 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

  • Internet of Things
  • Industrial Internet of Things
  • Industry 4.0
  • Smart Manufacturing
  • Predictive Maintenance
  • Digital Factories
  • Big Data in Industry 4.0
  • Cloud to Edge architectures in Industry 4.0
  • Machine learning and Artificial Intelligence in Industry 4.0
  • Cyber Physical Systems
  • Digital Twins
  • Business Process Modeling

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Published Papers (2 papers)

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32 pages, 18066 KiB  
Article
Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System
by Paula Fraga-Lamas, Daniel Barros, Sérgio Ivan Lopes and Tiago M. Fernández-Caramés
Sensors 2022, 22(21), 8500; https://doi.org/10.3390/s22218500 - 4 Nov 2022
Cited by 21 | Viewed by 4536
Abstract
While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex [...] Read more.
While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution (e.g., a good trade-off between cost, accuracy, resilience and computational efficiency) that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing. Full article
(This article belongs to the Special Issue IIoT Enabling Next Generation Digital Factories)
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Review

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18 pages, 1038 KiB  
Review
Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure
by Andrea Pinto, Luis-Carlos Herrera, Yezid Donoso and Jairo A. Gutierrez
Sensors 2023, 23(5), 2415; https://doi.org/10.3390/s23052415 - 22 Feb 2023
Cited by 32 | Viewed by 8136
Abstract
Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These [...] Read more.
Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years. Full article
(This article belongs to the Special Issue IIoT Enabling Next Generation Digital Factories)
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