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Industry 4.0: Sensor-Based Broadband Advances towards the Industrial Internet of Things Ecosystem

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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 1371

Special Issue Editors


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Guest Editor
1. Department of Informatics and Telecommunications, University of Thessaly, 382 21 Volos, Greece
2. Department of Computer Engineering and Informatics, University of Patras, 265 04 Rio Patras, Greece
Interests: 5G and beyond; convergence of optical and wireless networks; wireless broadband systems; channel modeling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Industrial Systems Institute of ATHENA Research and Innovation Center, 265 04 Patra, Greece
2. Human Opsis, 265 00 Patras, Greece
Interests: usable security; modeling; security and privacy; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of Industry 4.0, sensors play a pivotal role in enabling smart manufacturing processes and enhancing operational efficiency. However, the rapid evolution of Industry 4.0 brings forth emerging challenges and opens areas of interest for sensor technologies. One significant challenge is ensuring the interoperability and standardization of diverse sensor devices and communication protocols, fostering seamless integration within the industrial ecosystem. Another critical aspect is addressing the security and privacy concerns associated with the vast amount of data generated by sensors. As Industry 4.0 systems become more interconnected, the vulnerability to cyber threats necessitates robust security measures to safeguard sensitive information. Additionally, the development of energy-efficient and cost-effective sensor solutions remains an ongoing concern, especially in large-scale deployments. Exploring advanced sensing modalities, such as hyperspectral or quantum sensors, and enhancing sensor fusion techniques presents open areas of interest for further innovation in Industry 4.0 applications, promising to elevate the capabilities of sensors in supporting the future landscape of smart manufacturing. The scope of the present Special Issue is to feature novel results and comprehensive overview articles concerning recent advances and open challenges such as (but not limited to) the field of Industrial Internet of Things (IIoT), Industry 4.0 connectivity and broadband infrastructure, and sensor deployment in the context of industrial ecosystems.

Dr. Theofilos Chrysikos
Dr. George E. Raptis
Guest Editors

Manuscript Submission Information

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Keywords

  • IIoT
  • Industry 4.0 use cases
  • 5G
  • wireless sensor networks
  • wireless ad hoc and mesh networks for industrial solutions
  • protocols for industrial ecosystems
  • data acquisition and dissemination in Industry 4.0
  • Qos and QoE for industrial applications
  • digital control for industrial processes
  • hardware optimization for IoT devices in industrial ecosystems
  • security and privacy

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Published Papers (1 paper)

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Research

16 pages, 1145 KiB  
Article
Cross-Domain Knowledge Transfer for Sustainable Heterogeneous Industrial Internet-of-Things Networks
by Zhenzhen Gong, Qimei Cui and Wei Ni
Sensors 2024, 24(11), 3265; https://doi.org/10.3390/s24113265 - 21 May 2024
Viewed by 964
Abstract
In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each [...] Read more.
In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each utilizing a different energy source: grid power, green energy source, and mixed energy sources. Differing from mainstream algorithms that require consistency among groups, the proposed method enables knowledge transfer even across varying state and/or action spaces. With the advantage of multiple layers of knowledge extraction, a lightweight knowledge transfer is achieved without the need for neural networks. This facilitates broader applications in self-sustainable wireless networks. Simulation results reveal a notable improvement in the ’warm start’ policy for each equipment, manifesting as a 51.32% increase in initial reward compared to a random policy approach. Full article
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