research-article

AI-Aided Integrated Terrestrial and Non-Terrestrial 6G Solutions for Sustainable Maritime Networking

Authors:

Sergey AndreevAuthors Info & Claims

IEEE Network, Volume 36, Issue 3

Pages 183 - 190

https://doi.org/10.1109/MNET.104.2100351

Published: 01 May 2022 Publication History

Abstract

The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions, but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine-learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.

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Cited By

Yang HLin KXiao LZhao YXiong ZHan Z(2024)Energy Harvesting UAV-RIS-Assisted Maritime Communications Based on Deep Reinforcement Learning Against JammingIEEE Transactions on Wireless Communications10.1109/TWC.2024.336703423:8_Part_2(9854-9868)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TWC.2024.3367034
Polese MDohler MDressler FErol-Kantarci MJana RKnopp RMelodia T(2024)Empowering the 6G Cellular Architecture With Open RANIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.333461042:2(245-262)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/JSAC.2023.3334610
Mahboob SLiu L(2024)Revolutionizing Future Connectivity: A Contemporary Survey on AI-Empowered Satellite-Based Non-Terrestrial Networks in 6GIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334714526:2(1279-1321)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2023.3347145
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AI-Aided Integrated Terrestrial and Non-Terrestrial 6G Solutions for Sustainable Maritime Networking

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Published In

cover image IEEE Network: The Magazine of Global Internetworking

IEEE Network: The Magazine of Global Internetworking Volume 36, Issue 3

May/June 2022

198 pages

ISSN:0890-8044

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Copyright © 2022.

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IEEE Press

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Published: 01 May 2022

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Cited By

Yang HLin KXiao LZhao YXiong ZHan Z(2024)Energy Harvesting UAV-RIS-Assisted Maritime Communications Based on Deep Reinforcement Learning Against JammingIEEE Transactions on Wireless Communications10.1109/TWC.2024.336703423:8_Part_2(9854-9868)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TWC.2024.3367034
Polese MDohler MDressler FErol-Kantarci MJana RKnopp RMelodia T(2024)Empowering the 6G Cellular Architecture With Open RANIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.333461042:2(245-262)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/JSAC.2023.3334610
Mahboob SLiu L(2024)Revolutionizing Future Connectivity: A Contemporary Survey on AI-Empowered Satellite-Based Non-Terrestrial Networks in 6GIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334714526:2(1279-1321)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2023.3347145
Nauman AAlshahrani HNemri NOthman KAljehane NMaashi MDutta AAssiri MKhan W(2024)Dynamic resource management in integrated NOMA terrestrial–satellite networks using multi-agent reinforcement learningJournal of Network and Computer Applications10.1016/j.jnca.2023.103770221:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.jnca.2023.103770
Valente FLavacca FFiori TEramo V(2024)Proposal and investigation of a distributed learning strategy in Orbital Edge Computing-endowed satellite networks for Earth Observation applicationsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110625251:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110625
Dao NTu NThanh TBao VNa WCho S(2023)Neglected infrastructures for 6G—Underwater communicationsJournal of Network and Computer Applications10.1016/j.jnca.2023.103595213:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.jnca.2023.103595
Valente FEramo VLavacca F(2023)Optimal bandwidth and computing resource allocation in low earth orbit satellite constellation for earth observation applicationsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109849232:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109849
Shao SSu LGuo SYu PQiu X(2023)Multi-Agent Cooperative Game Based Task Computing Mechanism for UAV-Assisted 6G NTNMobile Networks and Applications10.1007/s11036-023-02264-x28:4(1510-1518)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11036-023-02264-x

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