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Maritime Communication Networks and 6G Technologies
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Maritime Communication Networks and 6G Technologies

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

Deadline for manuscript submissions: 10 September 2025 | Viewed by 5413

Special Issue Editors

Dr. Anastasios E. Giannopoulos

E-Mail Website
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
Dr. Nikolaos Nomikos

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
Dr. Panagiotis Trakadas

E-Mail Website
Guest Editor
Department of Ports Management and Shipping, National and Kapodistrian University of Athens, Athens, Greece
Interests: 5G technologies; wireless sensor networks; maritime communications; signal processing and routing protocols
Special Issues, Collections and Topics in MDPI journals

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

Published Papers (5 papers)

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Research

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23 pages, 2382 KiB  
Article
Deep Learning-Based Beam Selection in RIS-Aided Maritime Next-Generation Networks with Application in Autonomous Vessel Mooring
by Ioannis A. Bartsiokas, George K. Avdikos and Dimitrios V. Lyridis
J. Mar. Sci. Eng. 2025, 13(4), 754; https://doi.org/10.3390/jmse13040754 - 10 Apr 2025
Viewed by 432
Abstract
Maritime communication networks are critical for supporting the increasing demands of oceanic and coastal activities, including shipping, fishing, and offshore operations. However, traditional systems face significant challenges in providing reliable, high-throughput connectivity due to dynamic sea environments, mobility, and non-line-of-sight (NLoS) conditions. Reconfigurable [...] Read more.
Maritime communication networks are critical for supporting the increasing demands of oceanic and coastal activities, including shipping, fishing, and offshore operations. However, traditional systems face significant challenges in providing reliable, high-throughput connectivity due to dynamic sea environments, mobility, and non-line-of-sight (NLoS) conditions. Reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution to overcome these limitations by enabling programmable control of electromagnetic wave propagation in next-generation mobile communication networks, such as beyond fifth generation and sixth generation ones (B5G/6G). This paper presents a deep learning-based (DL) scheme for beam selection in RIS-aided maritime next-generation networks. The proposed approach leverages deep learning to optimize beam selection dynamically, enhancing signal quality, coverage, and network efficiency in complex maritime environments. By integrating RIS configurations with data-driven insights, the proposed framework adapts to changing channel conditions and potential vessel mobility while minimizing latency and computational overhead. Simulation results demonstrate significant improvements in both machine learning (ML) metrics, such as beam selection accuracy, and overall communication reliability compared to traditional methods. More specifically, the proposed scheme reaches around 99% Top-K Accuracy levels while jointly improving energy efficiency (ee) and spectral efficiency (SE) by approx. 2 times compared to state-of-the-art approaches. This study provides a robust foundation for employing DL in RIS-aided maritime networks, contributing to the advancement of intelligent, high-performance wireless communication systems for advanced maritime applications, such as autonomous mooring, the autonomous approach, and just-in-time arrival (JIT). Full article
(This article belongs to the Special Issue Maritime Communication Networks and 6G Technologies)
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28 pages, 8817 KiB  
Article
A Three-Dimensional Routing Protocol for Underwater Acoustic Sensor Networks Based on Fuzzy Logic Reasoning
by Lianyu Sun, Zhiyong Liu, Juan Dong and Jiayi Wang
J. Mar. Sci. Eng. 2025, 13(4), 692; https://doi.org/10.3390/jmse13040692 - 29 Mar 2025
Viewed by 257
Abstract
Underwater acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy [...] Read more.
Underwater acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol for three-dimensional (3D) UASNs. First, the FLRAF method redefines a conical forwarding region to prioritize nodes with greater effective advance distance, thereby reducing path deviations and minimizing the total number of hops. Unlike traditional approaches based on pipeline or hemispherical forwarding regions, this design ensures directional consistency in multihop forwarding, which improves transmission efficiency and energy utilization. Second, we design a nested fuzzy inference system for forwarding node selection. The inner inference system evaluates link quality by integrating the signal-to-noise ratio and some metrics related to the packet reception rate. This approach enhances robustness against transient fluctuations and provides a more stable estimation of link quality trends in dynamic underwater environments. The outer inference system incorporates link quality index, residual energy, and effective advance distance to rank candidate nodes. This multimetric decision model achieves a balanced trade-off between transmission reliability and energy efficiency. Simulation results confirm that the FLRAF method outperforms existing protocols under varying node densities and mobility conditions. It achieves a higher packet delivery rate, extended network lifetime, and lower energy consumption. These results demonstrate that the FLRAF method effectively addresses the challenges of energy constraints and unreliable links in 3D UASNs, making it a promising solution for adaptive and energy-efficient underwater communication. Full article
(This article belongs to the Special Issue Maritime Communication Networks and 6G Technologies)
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31 pages, 1843 KiB  
Article
Deep Q-Learning Based Adaptive MAC Protocol with Collision Avoidance and Efficient Power Control for UWSNs
by Wazir Ur Rahman, Qiao Gang, Feng Zhou, Muhammad Tahir, Wasiq Ali, Muhammad Adil and Muhammad Ilyas Khattak
J. Mar. Sci. Eng. 2025, 13(3), 616; https://doi.org/10.3390/jmse13030616 - 20 Mar 2025
Viewed by 445
Abstract
Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment [...] Read more.
Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications. Full article
(This article belongs to the Special Issue Maritime Communication Networks and 6G Technologies)
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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
Cited by 1 | Viewed by 1105
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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Review

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21 pages, 1364 KiB  
Review
Towards Predictive Maintenance in the Maritime Industry: A Component-Based Overview
by Alexandros S. Kalafatelis, Nikolaos Nomikos, Anastasios Giannopoulos, Georgios Alexandridis, Aikaterini Karditsa and Panagiotis Trakadas
J. Mar. Sci. Eng. 2025, 13(3), 425; https://doi.org/10.3390/jmse13030425 - 25 Feb 2025
Cited by 2 | Viewed by 2215
Abstract
The maritime industry has a significant influence on the global economy, underscoring the need for operational availability and safety through effective maintenance practices. Predictive maintenance emerges as a promising solution compared to conventional maintenance schemes currently employed by the industry, offering proactive failure [...] Read more.
The maritime industry has a significant influence on the global economy, underscoring the need for operational availability and safety through effective maintenance practices. Predictive maintenance emerges as a promising solution compared to conventional maintenance schemes currently employed by the industry, offering proactive failure predictions, reduced downtime events, and extended machinery lifespan. This paper addresses a critical gap in the existing literature by providing a comprehensive overview of the main data-driven PdM systems. Specifically, the review explores common issues found in vessel components (i.e., propulsion, auxiliary, electric, hull), examining how different state-of-the-art PdM architectures, ranging from basic machine learning models to advanced deep learning techniques aim to address them. Additionally, the concepts of centralized machine learning, federated, and transfer learning are also discussed, demonstrating their potential to enhance PdM systems as well as their limitations. Finally, the current challenges hindering adoption are discussed, together with the future directions to advance implementation in the field. Full article
(This article belongs to the Special Issue Maritime Communication Networks and 6G Technologies)
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