Next-Generation Industrial Wireless Communication

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 412

Special Issue Editor


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Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: Industrial Internet of Things/internet; wireless networks; intelligent perception; mobile computing

Special Issue Information

Dear Colleagues,

The advent of Industry 4.0 has sparked a revolution in industrial operations, emphasizing automation, real-time data processing, and enhanced connectivity. Wireless communication technologies are pivotal in this transformation, providing the backbone for seamless machine-to-machine (M2M) communication, real-time monitoring, and control systems. The transition to next-generation wireless technologies, including 5G, 6G, and beyond, promises to address the growing demands for higher data rates, lower latency, and increased reliability in industrial environments.

This Special Issue on "Next-Generation Industrial Wireless Communication" seeks to gather cutting-edge research and developments that push the boundaries of wireless communication technologies tailored to industrial applications. The focus will be on innovative solutions that enhance performance, reliability, and security in harsh industrial environments.

Scope and Topics:

This Special Issue invites original research articles, comprehensive reviews, and case studies on topics including, but not limited to, the following:

Advanced Wireless Technologies for Industry 4.0:

  • 5G and 6G applications in industrial settings;
  • Time sensitive network (TSN);
  • Wireless sensor networks (WSNs) and their integration with next-gen networks;
  • Backscatter communication and battery-free networks.
Network Architecture and Protocols:
  • New protocols and architectures for industrial wireless networks;
  • Software-defined networking (SDN) for industrial applications;
  • Edge computing and its impact on industrial wireless communication;
  • Industrial Internet of Things (IIoT) and network slicing.
Security and Reliability:
  • Cybersecurity challenges in industrial wireless networks;
  • Secure and reliable communication protocols;
  • Fault-tolerant and resilient network design;
  • Privacy-preserving technologies for industrial communication.
Applications and Case Studies:
  • Wireless communication in smart factories and digital twins;
  • Real-time monitoring and predictive maintenance using wireless networks;
  • AI-driven optimization of wireless communication in industrial settings;
  • Case studies on the deployment of next-gen wireless technologies in industry.
Emerging Trends and Future Directions:
  • Quantum communication for industrial networks;
  • Convergence of wireless communication with AI and machine learning;
  • Energy-efficient wireless communication technologies for industry;

Dr. Xiuzhen Guo
Guest Editor

Manuscript Submission Information

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Keywords

  • Industrial Internet of Things (IIoT)
  • wireless networking
  • next-generation communication

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Published Papers (1 paper)

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Research

12 pages, 642 KiB  
Article
Fast-Fading Modeling in Wireless Industrial Communications
by Mohammad Hossein Zadeh, Marina Barbiroli and Franco Fuschini
Electronics 2025, 14(7), 1378; https://doi.org/10.3390/electronics14071378 - 29 Mar 2025
Viewed by 212
Abstract
Wireless channel properties in industrial environments are significantly impacted by heavy machinery, leading to complex multipath propagation and strong blockage effects. Conventional empirical models employed in factory settings are constrained by their limited flexibility and applicability to diverse industrial conditions. In this study, [...] Read more.
Wireless channel properties in industrial environments are significantly impacted by heavy machinery, leading to complex multipath propagation and strong blockage effects. Conventional empirical models employed in factory settings are constrained by their limited flexibility and applicability to diverse industrial conditions. In this study, this limitation is tackled in a twofold way. First, machine learning algorithms, including linear regression and a Multi-Layer Perceptron, are employed to capture the complex relationships between fast-fading effects and key features of the industrial layout. Second, a flexible empirical formula is proposed to model fast-fading phenomena with enhanced adaptability, providing a comprehensive solution for diverse industrial contexts. The results align with previous studies and provide some trends in fast-fading sensitivity to different industrial features. The machine learning model demonstrates superior accuracy compared to the empirical formula, which nevertheless still achieves reasonable performance despite its simplicity. Full article
(This article belongs to the Special Issue Next-Generation Industrial Wireless Communication)
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