Maritime Communication Networks and 6G Technologies

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312).

Deadline for manuscript submissions: 20 March 2025 | Viewed by 1149

Special Issue Editors


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Guest Editor
Department of Ports Management and Shipping, National and Kapodistrian University of Athens, Athens, Greece
Interests: resource management; wireless network optimization; machine learning; maritime communications; multi-dimensional analysis

E-Mail Website
Guest Editor
Department of Ports Management and Shipping, National and Kapodistrian University of Athens, Athens, Greece
Interests: cooperative communications; maritime communication networks; low-latency communications and machine learning for wireless network optimization
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the advancements and future directions in maritime communication networks and the integration of 6G technologies. Our focus is on innovative solutions that enhance connectivity, data transmission, and operational efficiency in maritime environments. Topics include but are not limited to 6G-enabled communication protocols, IoT applications, cybersecurity, predictive maintenance, machine learning, and satellite communications.

This issue can fill a gap in the existing literature by providing a comprehensive overview of the latest research and developments in this rapidly evolving field. By situating the discussion within the context of current technological trends and maritime needs, this Special Issue can serve as a valuable resource for researchers, practitioners, and policymakers interested in the future of maritime communications.

We invite contributions that offer new insights, propose novel methodologies, and present case studies demonstrating the practical applications of 6G technologies in maritime contexts.

Dr. Anastasios E. Giannopoulos
Dr. Nikolaos Nomikos
Dr. Panagiotis Trakadas
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.

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. Journal of Marine Science and Engineering 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 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

  • maritime communication networks (MCNs)
  • machine learning and MCNs
  • 6G technologies in MCNs
  • multi-hop relaying techniques for MCNs
  • measurements and channel modeling for MCNs
  • radio resource management algorithms for MCNs

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Published Papers (1 paper)

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Research

16 pages, 3733 KiB  
Article
Research on Rapid Detection of Underwater Targets Based on Global Differential Model Compression
by Weishan Li, Yilin Li, Ruixue Li, Haozhe Shen, Wenjun Li and Keqiang Yue
J. Mar. Sci. Eng. 2024, 12(10), 1760; https://doi.org/10.3390/jmse12101760 - 4 Oct 2024
Viewed by 730
Abstract
Large-scale deep learning algorithms have emerged as the primary technology for underwater target detection, demonstrating exceptional inference effectiveness and accuracy. However, the real-time capabilities of these high-accuracy algorithms rely heavily on high-performance computing resources like CPUs and GPUs. It presents a challenge for [...] Read more.
Large-scale deep learning algorithms have emerged as the primary technology for underwater target detection, demonstrating exceptional inference effectiveness and accuracy. However, the real-time capabilities of these high-accuracy algorithms rely heavily on high-performance computing resources like CPUs and GPUs. It presents a challenge for deploying them on underwater embedded devices, where communication is limited and computational and energy resources are constrained. To overcome this, this paper focuses on constructing a lightweight yet highly accurate deep learning model suitable for real-time underwater target detection on edge devices. We develop a new lightweight model, named YOLO-TN, for real-time underwater object recognition on edge devices using a self-constructed image dataset captured by an underwater unmanned vehicle. This model is obtained by compressing the classical YOLO-V5, utilizing a globally differentiable deep neural architecture search method and a network pruning technique. Experimental results show that the YOLO-TN achieves a mean average precision (mAP) of 0.5425 and an inference speed of 28.6 FPS on embedded devices, while its parameter size is between 0.4 M and 0.6 M. This performance is a fifth of the parameter size and twelve times the FPS of the YOLO-V5 model, with almost no loss in inference accuracy. In conclusion, this framework significantly enhances the feasibility of deploying large-scale deep learning models on edge devices with high precision and compactness, ensuring real-time inference and offline deployment capabilities. This research is pivotal in addressing the computational challenges faced in underwater operations. Full article
(This article belongs to the Special Issue Maritime Communication Networks and 6G Technologies)
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