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Future Internet
Special Issues
Future Industrial Networks: Technologies, Algorithms, and Protocols
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Future Industrial Networks: Technologies, Algorithms, and Protocols

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 28 May 2026 | Viewed by 2505

Special Issue Editors

Dr. Luca Davoli

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
Interests: internet of things; smart agriculture; software-defined networking; big stream; security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gianluigi Ferrari

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; networking and communication; signal processing; smart systems
Special Issues, Collections and Topics in MDPI journals
Dr. Carlos Antonio Gomez Vega

E-Mail Website
Guest Editor
Department of Engineering and CNIT, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy
Interests: location awareness; resource optimization; signal processing; 6G
Dr. Andrea Giani

E-Mail Website
Guest Editor
Department of Engineering and CNIT, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy
Interests: quantum information science; quantum sensing and communications; statistical inference
Dr. Giampaolo Cuozzo

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering, Wi-Lab, CNIT, University of Bologna, 40136 Bologna, Italy
Interests: multiple-access schemes; radio resource management; scheduling; multi-hop protocols; reinforcement learning; deep learning; internet of things; THz networks
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Marini

E-Mail Website
Guest Editor
Wilab, CNIT and DEI Department, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Interests: UAV; IoT; digital twin; network automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial networks are crucial for the transformation of manufacturing plants into smart factories. These networks rely on heterogeneous technologies to provide ubiquitous communication, localization, sensing, and computing capabilities for a variety of use cases with diverse performance requirements. Nonetheless, industrial environments are impaired by heavy multi-path propagation and frequent non-line-of-sight conditions, with negative effects on system performance. Furthermore, industrial processes involve several fixed and mobile assets that must be monitored and controlled—for example, using Industrial Internet of Things (IIoT)-oriented devices and Supervisory Control and Data Acquisition (SCADA) systems. The goal of this Special Issue is to present effective technologies, algorithms, and protocols for context awareness and process monitoring powered by Machine Learning (ML) and Artificial Intelligence (AI).

This Special Issue seeks to explore theoretical and experimental aspects of industrial networks, including, but not limited to, the following:

  • Mobile radio network architectures and optimizations;
  • IIoT-oriented architectures and infrastructures;
  • Ultra-low-power solutions for IIoT networks;
  • Digital Twin-assisted predictions and optimizations for IIoT networks;
  • Network planning;
  • Network data analysis;
  • Implementation of wireless power transfer techniques;
  • Design of radio-over-fiber solutions;
  • Proof of Concepts for industrial networks;
  • AI in wireless communications and sensing;
  • AI-based context and location awareness;
  • AI-based process monitoring;
  • Quantum technologies and protocols for industrial networks;
  • Semantic and goal-oriented communications for IIoT applications;
  • Open, programmable, and AI-powered RAN for IIoT applications;
  • Computing infrastructures for IIoT applications;
  • End-to-end network slicing for IIoT applications;
  • Channel models for IIoT applications.

This Special Issue is partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRPP) of Next Generation EU (NGEU), partnership on "Telecommunications of the Future" (PE00000001—program "RESTART").

Dr. Luca Davoli
Prof. Dr. Gianluigi Ferrari
Dr. Carlos Antonio Gomez Vega
Dr. Andrea Giani
Dr. Giampaolo Cuozzo
Dr. Riccardo Marini
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. Future Internet 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 1800 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 networks
  • Industrial Internet of Things (IIoT)
  • mobile radio networks architecture
  • AI in wireless communication
  • Proof of Concepts (PoCs)

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

Published Papers (2 papers)

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Research

15 pages, 1872 KB  
Article
FPGA-Based Time Synchronization over Ethernet Networks for the DTT Control and Data Acquisition System
by Aamir Ali Patoli, Luca Boncagni, Gabriele Manduchi and Giancarlo Fortino
Future Internet 2026, 18(3), 159; https://doi.org/10.3390/fi18030159 - 18 Mar 2026
Viewed by 1060
Abstract
Time synchronization is a fundamental requirement for the reliable operation of Control and Data Acquisition Systems (CODASs) in large-scale fusion experiments such as the Divertor Tokamak Test (DTT). Distributed diagnostics, sensors, and control subsystems must share a unified time reference to guarantee deterministic [...] Read more.
Time synchronization is a fundamental requirement for the reliable operation of Control and Data Acquisition Systems (CODASs) in large-scale fusion experiments such as the Divertor Tokamak Test (DTT). Distributed diagnostics, sensors, and control subsystems must share a unified time reference to guarantee deterministic data acquisition and stable plasma control. This paper presents the FPGA-based implementation and evaluation of a synchronization system that combines the IEEE 1588 Precision Time Protocol (PTP) with Pulse Per Second (PPS) generation. The proposed platform is built on Zynq UltraScale+ Kria KR260 System-on-Modules (SOMs) running a customized PetaLinux distribution with LinuxPTP utilities. Hardware timestamping is enabled through the integrated Timestamping Unit (TSU) in the Gigabit Ethernet MAC, while a hardware logic module generates PPS signals from the synchronized PTP clock. Experimental validation demonstrates nanosecond-level synchronization with an RMS timing accuracy of approximately 8.5 ns. A detailed analysis of PPS offset, network path delay, and servo adjustments confirms stability of the timing system. The proposed design offers a low-cost, flexible, fully customizable and controllable solution for distributed diagnostic and control systems in fusion facilities. Full article
(This article belongs to the Special Issue Future Industrial Networks: Technologies, Algorithms, and Protocols)
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36 pages, 630 KB  
Article
Semantic Communication Unlearning: A Variational Information Bottleneck Approach for Backdoor Defense in Wireless Systems
by Sümeye Nur Karahan, Merve Güllü, Mustafa Serdar Osmanca and Necaattin Barışçı
Future Internet 2026, 18(1), 17; https://doi.org/10.3390/fi18010017 - 28 Dec 2025
Viewed by 938
Abstract
Semantic communication systems leverage deep neural networks to extract and transmit essential information, achieving superior performance in bandwidth-constrained wireless environments. However, their vulnerability to backdoor attacks poses critical security threats, where adversaries can inject malicious triggers during training to manipulate system behavior. This [...] Read more.
Semantic communication systems leverage deep neural networks to extract and transmit essential information, achieving superior performance in bandwidth-constrained wireless environments. However, their vulnerability to backdoor attacks poses critical security threats, where adversaries can inject malicious triggers during training to manipulate system behavior. This paper introduces Selective Communication Unlearning (SCU), a novel defense mechanism based on Variational Information Bottleneck (VIB) principles. SCU employs a two-stage approach: (1) joint unlearning to remove backdoor knowledge from both encoder and decoder while preserving legitimate data representations, and (2) contrastive compensation to maximize feature separation between poisoned and clean samples. Extensive experiments on the RML2016.10a wireless signal dataset demonstrate that SCU achieves 629.5 ± 191.2% backdoor mitigation (5-seed average; 95% CI: [364.1%, 895.0%]), with peak performance of 1486% under optimal conditions, while maintaining only 11.5% clean performance degradation. This represents an order-of-magnitude improvement over detection-based defenses and fundamentally outperforms existing unlearning approaches that achieve near-zero or negative mitigation. We validate SCU across seven signal processing domains, four adaptive backdoor types, and varying SNR conditions, demonstrating unprecedented robustness and generalizability. The framework achieves a 243 s unlearning time, making it practical for resource-constrained edge deployments in 6G networks. Full article
(This article belongs to the Special Issue Future Industrial Networks: Technologies, Algorithms, and Protocols)
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