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Industrial Internet of Things (IIoT) Platforms and Application—Second Edition

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

Deadline for manuscript submissions: 10 November 2025 | Viewed by 2365

Special Issue Editors


E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 70013 Heraklion, Greece
Interests: communications and networking; Internet of Things; pervasive and physical computing; sensor networks; industrial informatics; location and context awareness; informatics in education
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 70013 Heraklion, Greece
Interests: edge networking; cyber security; public safety; digital video broadcasting; edge computing; SDN; NFV; Internet of Things; network management; network virtualization
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Athens, Athens, Greece
Interests: mobile networks; future internet/NGI; cognitive management; autonomic communications; reconfigurable mobile systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Following the success of the previous Special Issue “Industrial Internet of Things (IIoT) Platforms and Applications”, we are pleased to announce the next in the series, entitled “Industrial Internet of Things (IIoT) Platforms and Application—Second Edition”. 

In the modern landscape of Industry 4.0, monolithic and vendor-specific industrial control systems (ICSs) of the past, which feature little or no interaction with the internet world, have been utilized to create a digitally interconnected and software-defined control ecosystem. In such highly distributed and heterogeneous environments, specialized modular software enables the centralized management and orchestration of the available services and infrastructures that control the manufacturing process. The latter provides a unified interoperable intelligent framework for the integration of operational technology (OT) with information technology (IT); this can ideally enable vendor-agnostic and policy-driven infrastructure control, as well as provide monitoring, decision, execution, and reporting services for large-scale workloads and product lifecycle management. The integration of OT with IT benefits industries by reducing costs and risks, and enhancing their performance and flexibility. A critical trend that boosts OT and IT convergence in the context of smart industries is the emergence of the Industrial Internet of Things (IIoT). IIoT refers to the evolution of typical ICSs; as such, interconnected sensors, actuators, controllers, PLCs, instruments, and other field devices are networked together with industrial applications. Internetworking technologies comprise traditional serial protocols (e.g., RS232/485) and fieldbus topologies (e.g., Modbus, Profibus, and CAN) to packet data protocols (e.g., PROFINET and Industrial Ethernet), TCP/IP integration (e.g., VLANs, VPN, remote access, and QoS), and wireless connectivity (e.g., WLAN, 802.15.4, and LPWAN). This connectivity allows for a higher degree of automation via data collection, exchange, and analysis. Furthermore, the introduction of the IIoT into industrial environments has increased the intimacy between data processing and field devices, so that the response time is enhanced and the bandwidth reduced; this offers the opportunity to employ edge/fog computing in industrial applications. However, the emergence of this evolution comes with a price: novel risks and cyber-security threads abound at the different layers of ICSs, and industrial employers should become aware of these issues. 

Hence, IIoT is an umbrella term that incorporates advances in various technological fields such as wireless and computer networking, sensor networks, cyber-physical systems, cloud and edge computing, big data analytics, artificial intelligence and machine learning, and cybersecurity. 

The aim of this Special Issue is to present high-quality, state-of-the-art research papers that address challenging issues regarding the Internet of Things for Industry-4.0-oriented applications. The scope of this Special Issue includes, but is not limited to, the following: 

  • Advances in the Internet of Things for industrial applications;
  • Sensor networking for Industry 4.0 applications;
  • Advances concerning the various smart industries (smart factories, manufacturing, healthcare, agriculture, farming, cities, grids, etc.);
  • Empirical studies from the deployment of IIoT applications in industrial environments;
  • Advanced wireless networking for industrial use;
  • Communication and networking issues for industrial environments;
  • Network management issues for Industry 4.0 environments;
  • Edge/fog/cloud computing for Industry 4.0;
  • Network function virtualization (NFV) and software-defined networking (SDN) issues for industrial use;
  • Cybersecurity issues and solutions for Industry 4.0 environments;
  • Advances concerning the convergence of OT/IT in Industry 4.0 environments;
  • Distributed ICSs for Industry 4.0;
  • Human–machine interfaces (HMI) and SCADA supervisory systems for Industry 4.0;
  • Augmented and virtual reality issues for Industry 4.0 applications;
  • Machine learning, artificial, and computational intelligence for use in Industry 4.0 applications;
  • Predictive diagnostics and maintenance tools for Industry 4.0;
  • Advanced data repository and data analytics tools for Industry 4.0 applications;
  • Supply chain management for Industry 4.0. 

Dr. Spyros Panagiotakis
Dr. Evangelos K. Markakis
Dr. Nancy Alonistioti
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.

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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

  • industrial informatics
  • Industry 4.0
  • OT/IT convergence
  • Internet of Things
  • sensor networks
  • computer networks
  • wireless communications
  • network management
  • network function virtualization and software-defined networking
  • cybersecurity
  • predictive maintenance
  • edge/fog/cloud computing
  • smart industries (factories, manufacturing, healthcare, agriculture, farming, cities, grids, etc.)
  • machine learning, artificial and computational intelligence
  • augmented and virtual reality
  • supply chain management
  • data analytics
  • human–computer interaction

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Related Special Issue

Published Papers (3 papers)

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Research

25 pages, 11142 KiB  
Article
Enhanced Heat-Powered Batteryless IIoT Architecture with NB-IoT for Predictive Maintenance in the Oil and Gas Industry
by Raúl Aragonés, Joan Oliver and Carles Ferrer
Sensors 2025, 25(8), 2590; https://doi.org/10.3390/s25082590 - 19 Apr 2025
Viewed by 163
Abstract
The carbon footprint associated with human activity, particularly from energy-intensive industries such as iron and steel, aluminium, cement, oil and gas, and petrochemicals, contributes significantly to global warming. These industries face unique challenges in achieving Industry 4.0 goals due to the widespread adoption [...] Read more.
The carbon footprint associated with human activity, particularly from energy-intensive industries such as iron and steel, aluminium, cement, oil and gas, and petrochemicals, contributes significantly to global warming. These industries face unique challenges in achieving Industry 4.0 goals due to the widespread adoption of industrial Internet of Things (IIoT) technologies, which require reliable and efficient power solutions. Conventional wireless devices powered by lithium batteries have limitations, including a reduced lifespan in high-temperature environments, incompatibility with explosive atmospheres, and high maintenance costs. This paper proposes a novel approach to address these challenges by leveraging residual heat to power IIoT devices, eliminating the need for batteries and enabling autonomous operation. Based on the Seebeck effect, thermoelectric energy harvesters transduce waste heat from industrial surfaces, such as pipes or chimneys, into sufficient electrical energy to power IoT nodes for applications like the condition monitoring and predictive maintenance of rotating machinery. The methodology presented standardises the modelling and simulation of Waste Heat Recovery Systems (IoT-WHRSs), demonstrating their feasibility through statistical analysis of IoT-WHRS architectures. Furthermore, this technology has been successfully implemented in a petroleum refinery, where it benefits from the NB-IoT standard for long-range, robust, and secure communications, ensuring reliable data transmission in harsh industrial environments. The results highlight the potential of this solution to reduce costs, improve safety, and enhance efficiency in demanding industrial applications, making it a valuable tool for the energy transition. Full article
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19 pages, 708 KiB  
Article
Efficient Collaborative Learning in the Industrial IoT Using Federated Learning and Adaptive Weighting Based on Shapley Values
by Dost Muhammad Saqib Bhatti, Mazhar Ali, Junyong Yoon and Bong Jun Choi
Sensors 2025, 25(3), 969; https://doi.org/10.3390/s25030969 - 6 Feb 2025
Viewed by 729
Abstract
The integration of the Industrial Internet of Things (IIoT) and federated learning (FL) can be a promising approach to achieving secure and collaborative AI-driven Industry 4.0 and beyond. FL enables the collaborative training of a global model under the supervision of a central [...] Read more.
The integration of the Industrial Internet of Things (IIoT) and federated learning (FL) can be a promising approach to achieving secure and collaborative AI-driven Industry 4.0 and beyond. FL enables the collaborative training of a global model under the supervision of a central server while ensuring that data remain localized to ensure data privacy. Subsequently, the locally trained models can be aggregated to enhance the global model training process. Nevertheless, the merging of these local models can significantly impact the efficacy of global training due to the diversity of each industry’s data. In order to enhance robustness, we propose a Shapley value-based adaptive weighting mechanism that trains the global model as a sequence of cooperative games. The client weights are adjusted based on their Shapley contributions as well as the size and variability of their local datasets in order to improve the model performance. Furthermore, we propose a quantization strategy to mitigate the computational expense of Shapley value computation. Our experiments demonstrate that our method achieves the highest accuracy compared to existing methods due to the efficient assignment of weights. Additionally, our method achieves nearly the same accuracy with significantly lower computational cost by reducing the computation overhead of Shapley value computation in each round of training. Full article
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22 pages, 8214 KiB  
Article
Transforming Industrial Maintenance with Thermoelectric Energy Harvesting and NB-IoT: A Case Study in Oil Refinery Applications
by Raúl Aragonés, Joan Oliver and Carles Ferrer
Sensors 2025, 25(3), 703; https://doi.org/10.3390/s25030703 - 24 Jan 2025
Viewed by 981
Abstract
Heat-intensive industries (e.g., iron and steel, aluminum, cement) and explosive sectors (e.g., oil and gas, chemical, petrochemical) face challenges in achieving Industry 4.0 goals due to the widespread adoption of industrial Internet of Things (IIoT) technologies. Wireless solutions are favored in large facilities [...] Read more.
Heat-intensive industries (e.g., iron and steel, aluminum, cement) and explosive sectors (e.g., oil and gas, chemical, petrochemical) face challenges in achieving Industry 4.0 goals due to the widespread adoption of industrial Internet of Things (IIoT) technologies. Wireless solutions are favored in large facilities to reduce the costs and complexities of extensive wiring. However, conventional wireless devices powered by lithium batteries have limitations, including reduced lifespan in high-temperature environments and incompatibility with explosive atmospheres, leading to high maintenance costs. This paper presents a novel approach for energy-intensive and explosive industries, which represent over 40% of the gross production revenue (GPR) in several countries. The proposed solution uses residual heat to power ATEX-certified IIoT devices, eliminating the need for batteries and maintenance. These devices are designed for condition monitoring and predictive maintenance of rotating machinery, which is common in industrial settings. The study demonstrates the successful application of this technology, highlighting its potential to reduce costs and improve safety and efficiency in challenging industrial environments. Full article
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