Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Advanced Industry 4.0/5.0: Intelligence and Automation
share announcement

Advanced Industry 4.0/5.0: Intelligence and Automation

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 5164

Special Issue Editors

Dr. Isaías González Pérez

E-Mail
Guest Editor
Department of Electrical, Electronical and Automatic Engineering, School of Industrial Engineering, University of Extremadura, Avda de Elvas s/n, 06006 Badajoz, Spain
Interests: smart grids and microgrids; renewable energy; industrial control and automation; monitoring and supervision; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio José Calderón Godoy

E-Mail
Guest Editor
Department of Electrical, Electronical and Automatic Engineering, School of Industrial Engineering, University of Extremadura, Avda de Elvas s/n, 06006 Badajoz, Spain
Interests: smart grids and microgrids; renewable energy; intelligent control; industrial control and automation; monitoring and supervision; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Javier Folgado Gaspar

E-Mail Website
Guest Editor
Department of Electrical Engineering, Electronics and Automation, University of Extremadura, Avenida de Elvas, s/n, 06006 Badajoz, Spain
Interests: smart microgrids; pem fuel cell; IoT; pv generator; Industry 4.0

Special Issue Information

Dear Colleagues,

The rise and development of the Industry 4.0 paradigm and technologies related to the Internet of Things (IoT) have led to significant advances in all sectors. Regarding the industrial field, IoT applications are referred to as the Industrial Internet of things (IIoT), highlighting applications oriented to the interconnectivity of devices, systems, processes, etc. This Special Issue is primarily focused on the advancements and innovations in this context on topics such as automation, monitoring and supervision, smart technologies, and IoT applications. For instance, novel approaches involving modern automation and control approaches, open IoT platforms, or building digital replicas (also called digital twins) constitute current trends.

We would like to invite the submission of high-quality manuscripts from researchers, engineers, and industry professionals for publication in this Special Issue. The manuscripts should be unpublished and report significant research progress. The key criteria for paper acceptance will be novelty. Manuscripts reporting experimental proofs, results, and lessons learned are strongly encouraged. Review papers on the state-of-the-art of different topics related to Industry 4.0/5.0 are also welcome. The main topics of interest include, but are not limited to, the following:

  • Industry 4.0/5.0;
  • Industrial Internet and Industrial Internet of Things;
  • Prognosis, predictive maintenance, condition monitoring, and fault diagnosis;
  • Remote access, cybersecurity, and privacy in Industry 4.0/5.0 environments;
  • Industrial wireless networks or related energy technologies;
  • Advanced manufacturing systems;
  • Intelligent sensors;
  • Blockchain technologies;
  • Automation control systems;
  • Artificial intelligence;
  • Power electronics applications in the Industry 4.0/5.0;
  • Advanced sensors and wired/wireless sensor networks for Industry 4.0/5.0;
  • Gateways and fog/edge/cloud computing for Industry 4.0/5.0;
  • Machine learning, artificial and computational intelligence for industry 4.0/5.0;
  • Industrial Big Data, aggregation, and analytics;
  • Digitalization, virtualization and simulation of processes, and digital twins;
  • Low-cost, open source, and IoT technologies for applications in automation;
  • Interoperability and convergence of OT/IT in Industry 4.0/5.0;
  • SCADA systems, real-time monitoring, and HMI/GUI for Industry 4.0/5.0;
  • Smart manufacturing and industrial cyber–physical systems (ICPS);
  • Middleware, architectures, fieldbuses, and communication protocols;
  • Applications and use cases (smart factories, smart grids/microgrids, smart cities, smart and digital agriculture, Industry 4.0/5.0-oriented education, etc.).

Technical Program Committee Member:

Mr. David Calderón González,  University of Extremadura, Spain

Dr. Isaías González Pérez
Dr. Antonio José Calderón Godoy
Dr. Francisco Javier Folgado Gaspar
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. Electronics 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

  • Industry 4.0/5.0
  • artificial intelligence
  • blockchain technologies
  • intelligent sensors
  • interoperability
  • automation
  • monitoring
  • digital twin
  • prognosis and diagnosis
  • control technology
  • cloud/edge computing
  • open-source
  • industrial internet of things
  • industrial cyber–physical systems (ICPS)
  • industrial sensor networks

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 2922 KiB  
Article
Enhancing Security of Automotive OTA Firmware Updates via Decentralized Identifiers and Distributed Ledger Technology
by Ana Kovacevic and Nenad Gligoric
Electronics 2024, 13(23), 4640; https://doi.org/10.3390/electronics13234640 - 25 Nov 2024
Viewed by 740
Abstract
The increasing connectivity and complexity of automotive systems require enhanced mechanisms for firmware updates to ensure security and integrity. Traditional methods are insufficient for modern vehicles that require seamless over-the-air (OTA) updates. Current OTA mechanisms often lack robust security measures, leaving vehicles vulnerable [...] Read more.
The increasing connectivity and complexity of automotive systems require enhanced mechanisms for firmware updates to ensure security and integrity. Traditional methods are insufficient for modern vehicles that require seamless over-the-air (OTA) updates. Current OTA mechanisms often lack robust security measures, leaving vehicles vulnerable to attacks. This paper proposes an innovative approach based on the use of decentralized identifiers (DIDs) and distributed ledger technology (DLT) for secure OTA firmware updates of on-vehicle software. By utilizing DIDs for unique vehicle identification, as well as verifiable credentials (VCs) and verifiable presentations (VPs) for secure information exchange and verification, the solution ensures the integrity and authenticity of software updates. It also allows for the revocation of specific updates, if necessary, thereby improving overall security. The security analysis applied the STRIDE methodology, which enabled the identification of potential threats, including spoofing, tampering, and privilege escalation. The results showed that our solution effectively mitigates these threats, while a performance evaluation indicated low latency during operations. Full article
(This article belongs to the Special Issue Advanced Industry 4.0/5.0: Intelligence and Automation)
►▼ Show Figures

Figure 1

17 pages, 3949 KiB  
Article
Smart Sleep Monitoring: An Integrated Application for Tracking and Analyzing Babies’ Sleep—BabyCare
by Lukáš Beňo, Erik Kučera and Matej Bašista
Electronics 2024, 13(21), 4210; https://doi.org/10.3390/electronics13214210 - 27 Oct 2024
Viewed by 768
Abstract
This article presents an innovative application designed to assist parents in monitoring and analyzing their children’s sleep patterns, contributing to insights into their health and development. The application integrates a hardware solution that captures sleep data through sensors. These data are then processed, [...] Read more.
This article presents an innovative application designed to assist parents in monitoring and analyzing their children’s sleep patterns, contributing to insights into their health and development. The application integrates a hardware solution that captures sleep data through sensors. These data are then processed, analyzed, and securely stored in a cloud database. Key features of the application include real-time monitoring of the child’s sleep status, historical sleep data visualization through graphical representations, and alert notifications for any detected abnormalities. The system offers a comprehensive tool for parents to ensure the well-being of their children by providing valuable sleep-related information. Full article
(This article belongs to the Special Issue Advanced Industry 4.0/5.0: Intelligence and Automation)
►▼ Show Figures

Figure 1

24 pages, 4205 KiB  
Article
Using Mixed Reality for Control and Monitoring of Robot Model Based on Robot Operating System 2
by Dominik Janecký, Erik Kučera, Oto Haffner, Erika Výchlopeňová and Danica Rosinová
Electronics 2024, 13(17), 3554; https://doi.org/10.3390/electronics13173554 - 6 Sep 2024
Viewed by 904
Abstract
This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the [...] Read more.
This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the Unity game engine alongside the Mixed Reality Toolkit (MRTK) to create an immersive mixed reality application. The project uses the Turtlebot 3 Burger model robot, simulated within the Gazebo virtual environment, as a representative mechatronic system for demonstration purposes. Communication between the mixed reality application and ROS 2 is facilitated through a publish–subscribe mechanism, utilizing ROS TCP Connector for message serialization between nodes. This interface not only enhances the user experience by allowing for the real-time monitoring and control of the robotic system but also aligns with the principles of Industry 5.0, emphasizing human-centric and inclusive technological advancements. The practical outcomes of this research include a fully functional mixed reality application that integrates seamlessly with ROS 2, showcasing the potential of mixed reality technologies in advancing the field of industrial automation and human–machine interaction. Full article
(This article belongs to the Special Issue Advanced Industry 4.0/5.0: Intelligence and Automation)
►▼ Show Figures

Figure 1

Review

Jump to: Research

18 pages, 3413 KiB  
Review
Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review
by Izabela Rojek, Dariusz Mikołajewski, Adam Mroziński and Marek Macko
Electronics 2024, 13(16), 3338; https://doi.org/10.3390/electronics13163338 - 22 Aug 2024
Cited by 2 | Viewed by 2220
Abstract
Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising [...] Read more.
Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics. Full article
(This article belongs to the Special Issue Advanced Industry 4.0/5.0: Intelligence and Automation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop