Industrial Internet of Things and Smart Technologies in Industry 4.0/5.0: Advanced Monitoring and Automation Trends and Applications

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 2447

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Electronics and Automation, University of Extremadura, Avenida de Elvas, s/n, 06006 Badajoz, Spain
Interests: industrial control and automation; monitoring and supervision; Industry 4.0; Internet of Things; Industrial Internet of Things; smart grids and microgrids; renewable energy
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
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: industrial control and automation; monitoring and supervision; Industry 4.0; Internet of Things; Industrial Internet of Things; smart grids and microgrids; renewable energy
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
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: industrial control and automation; monitoring and supervision; Industry 4.0; Internet of Things; Industrial Internet of Things; smart grids and microgrids; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rise and development of the Industry 4.0/5.0 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), which are tailored toward the interconnectivity of devices, systems, processes, etc. This Special Issue is primary focused on the advancements and innovations in this context on topics such as automation, monitoring and supervision, smart technologies, and (I)IoT applications. For instance, novel approaches, involving modern automation and control approaches; open IoT platforms and environments for monitoring and supervision purposes; or building digital replicas (also called digital twins), constitute the 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 criterion 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;
  • Internet of Things (IoT) and Industrial Internet of Things (IIoT);
  • Low-cost, open-source, and IoT technologies for applications in automation;
  • SCADA systems, real-time monitoring, and HMI/GUI for Industry 4.0/5.0;
  • Middleware, architectures, fieldbuses, and communication protocols;
  • Automation control systems;
  • Intelligent sensors;
  • Digitalization, virtualization, and simulation of processes, and digital twins;
  • Industrial big data, aggregation, and analytics;
  • Advanced sensors and wired/wireless sensor networks for Industry 4.0/5.0;
  • Artificial intelligence, machine learning, and computational intelligence for Industry 4.0/5.0;
  • 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. Francisco Javier Folgado Gaspar
Dr. Antonio José Calderón Godoy
Dr. Isaías González Pérez
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 250 words) can be sent to the Editorial Office for assessment.

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

  • Internet of Things
  • Industrial Internet of Things
  • automation
  • monitoring and supervision
  • Industry 4.0/5.0
  • open source
  • smart technologies

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Published Papers (1 paper)

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Research

33 pages, 2342 KB  
Article
A Digital Twins Platform for Digital Manufacturing
by Maheshi Gunaratne, Dimitrios Georgakopoulos and Abhik Banerjee
Electronics 2026, 15(3), 583; https://doi.org/10.3390/electronics15030583 - 29 Jan 2026
Viewed by 1771
Abstract
Digital manufacturing aims to make manufacturing more productive, resilient, and competitive by improving production efficiency and enhancing product quality. To achieve this, this paper proposes a novel digital twin framework for representing complex industrial machines, materials, and products in manufacturing production lines. In [...] Read more.
Digital manufacturing aims to make manufacturing more productive, resilient, and competitive by improving production efficiency and enhancing product quality. To achieve this, this paper proposes a novel digital twin framework for representing complex industrial machines, materials, and products in manufacturing production lines. In the framework, digital twins comprise a physical twin, a virtual twin, and digital threads interconnecting these. The physical twin incorporates relevant physical entities in manufacturing production lines, such as production machines, a material, or a product, as well as additional attached sensors needed for measuring the properties of the physical twin. The virtual twin, which contains the description of physical twin, such as the product’s properties, and AI models use the measurements collected from the production machine or the sensors in the physical twin to optimize production efficiency and ensure products consistency/quality. The digital threads provide for bidirectional communication using industrial protocols between the physical and virtual entities, and also between the AI model(s) and the orchestration component of the virtual twin. The paper also proposes a digital twin-based platform for digital manufacturing. The platform supports the creation and lifecycle management of digital twins for the production machines, materials, and products in each production line. In addition, the platform for digital manufacturing supports the development and management of digital manufacturing solutions that enhance the productivity and resiliency of entire production lines. The paper presents a case study from the composite airframe manufacturing sector that includes a sample framework-based implementation of a digital twin of an airframe part product. This product digital twin incorporates sensors that measure the temperature and viscosity of the composite product and AI model that used these real-time measurements to predict the product quality and reduce its curing time, ensuring both the product quality and production efficiency. Full article
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