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Artificial Intelligence Challenges to the Industrial Internet of Things and...
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Artificial Intelligence Challenges to the Industrial Internet of Things and Industrial Control Systems Applications

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 2452

Special Issue Editor

Dr. Fiammetta Marulli

E-Mail Website
Guest Editor
Department of Mathematics and Physics, Università della Campania “Luigi Vanvitelli”, Viale Lincoln, 81100 Caserta, Italy
Interests: artificial intelligence; machine and deep learning; federated deep learning on cloud systems; data analytics and data science applied to Internet of Things and cyber-physical systems; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect both academic and industrial reserach contributions to build a reference and actual picture of the main challenges and the main barriers—in terms of both operational and technology—to adopt nowadays and interface profitably Artificial Intelligence opportunities to the IoT, IIoT, Edge Computing, in a way that can lead industrial companies to transform Industry 4.0 promises into reality. Additionally, it would be focused on the opportunities, limitations and hidden b-side effects in applying AI to Industrial Control Systems, with specific attention to the cyber security issues introduced when very complex and so different systems and technologies are put together to work synergically.

Dr. Fiammetta Marulli
Guest Editor

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. AI is an international peer-reviewed open access monthly 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 1600 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

  • Artificial Intelligence
  • Machine Learning
  • Internet of Things
  • Industrial Internet of Things
  • Industrial Control Systems
  • Industry 4.0
  • applied research
  • cybersecurity
  • security

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Published Papers (3 papers)

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Research

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22 pages, 6988 KiB  
Article
A Hybrid and Modular Integration Concept for Anomaly Detection in Industrial Control Systems
by Christian Goetz and Bernhard G. Humm
AI 2025, 6(5), 91; https://doi.org/10.3390/ai6050091 (registering DOI) - 27 Apr 2025
Viewed by 81
Abstract
Effective anomaly detection is essential for realizing modern and secure industrial control systems. However, the direct integration of anomaly detection within such a system is complex due to the wide variety of hardware used, different communication protocols, and given industrial requirements. Many components [...] Read more.
Effective anomaly detection is essential for realizing modern and secure industrial control systems. However, the direct integration of anomaly detection within such a system is complex due to the wide variety of hardware used, different communication protocols, and given industrial requirements. Many components of an industrial control system allow direct integration, while others are designed as closed systems or do not have the required performance. At the same time, the effective usage of available resources and the sustainable use of energy are more important than ever for modern industry. Therefore, in this paper, we present a modular and hybrid concept that enables the integration of efficient and effective anomaly detection while optimising the use of available resources under consideration of industrial requirements. Because of the modular and hybrid properties, many functionalities can be outsourced to the respective devices, and at the same time, additional hardware can be integrated where required. The resulting flexibility allows the seamless integration of complete anomaly detection into existing and legacy systems without the need for expensive centralised or cloud-based solutions. Through a detailed evaluation within an industrial unit, we demonstrate the performance and versatility of our concept. Full article
(This article belongs to the Special Issue Artificial Intelligence Challenges to the Industrial Internet of Things and Industrial Control Systems Applications)
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28 pages, 4142 KiB  
Article
IntelliGrid AI: A Blockchain and Deep-Learning Framework for Optimized Home Energy Management with V2H and H2V Integration
by Sami Binyamin and Sami Ben Slama
AI 2025, 6(2), 34; https://doi.org/10.3390/ai6020034 - 12 Feb 2025
Viewed by 930
Abstract
The integration of renewable energy sources and electric vehicles has become a focal point for industries and academia due to its profound economic, environmental, and technological implications. These developments require the development of a robust intelligent home energy management system (IHEMS) to optimize [...] Read more.
The integration of renewable energy sources and electric vehicles has become a focal point for industries and academia due to its profound economic, environmental, and technological implications. These developments require the development of a robust intelligent home energy management system (IHEMS) to optimize energy utilization, enhance transaction security, and ensure grid stability. For this reason, this paper develops an IntelliGrid AI, an advanced system that integrates blockchain technology, deep learning (DL), and dual-energy transmission capabilities—vehicle to home (V2H) and home to vehicle (H2V). The proposed approach can dynamically optimize household energy flows, deploying real-time data and adaptive algorithms to balance energy demand and supply. Blockchain technology ensures the security and integrity of energy transactions while facilitating decentralized peer-to-peer (P2P) energy trading. The core of IntelliGrid AI is an advanced Q-learning algorithm that intelligently allocates energy resources. V2H enables electric vehicles to power households during peak periods, reducing the strain on the grid. Conversely, H2V technology facilitates the efficient charging of electric cars during peak hours, contributing to grid stability and efficient energy utilization. Case studies conducted in Tunisia validate the system’s performance, showing a 20% reduction in energy costs and significant improvements in transaction efficiency. These results highlight the practical benefits of integrating V2H and H2V technologies into innovative energy management frameworks. Full article
(This article belongs to the Special Issue Artificial Intelligence Challenges to the Industrial Internet of Things and Industrial Control Systems Applications)
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Review

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23 pages, 3001 KiB  
Review
A Bibliometric Analysis on Artificial Intelligence in the Production Process of Small and Medium Enterprises
by Federico Briatore, Marco Tullio Mosca, Roberto Nicola Mosca and Mattia Braggio
AI 2025, 6(3), 54; https://doi.org/10.3390/ai6030054 - 12 Mar 2025
Viewed by 673
Abstract
Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the [...] Read more.
Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the manufacturing sector, such as the demand for customized products, market volatility and sustainability goals, and the integration of artificial intelligence and Big Data. This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, with no time limitation, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries. However, the adoption of modern technologies, particularly AI, can be challenging for them, due to the intrinsic structure of this type of enterprise, despite the positive effects obtained in large organizations. Full article
(This article belongs to the Special Issue Artificial Intelligence Challenges to the Industrial Internet of Things and Industrial Control Systems Applications)
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