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

التفاصيل البيبلوغرافية
العنوان: AI-Aided Integrated Terrestrial and Non-Terrestrial 6G Solutions for Sustainable Maritime Networking
المؤلفون: Salwa Saafi, Olga Vikhrova, Gabor Fodor, Jiri Hosek, Sergey Andreev
المساهمون: Tampere University, Electrical Engineering
بيانات النشر: arXiv, 2022.
سنة النشر: 2022
مصطلحات موضوعية: Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Terrestrial/non-terrestrial networks, Computer Networks and Communications, Computer Science - Artificial Intelligence, 213 Electronic, automation and communications engineering, electronics, ComputerApplications_COMPUTERSINOTHERSYSTEMS, sustainability, Maritime, Computer Science - Networking and Internet Architecture, Energy efficiency, Artificial Intelligence (cs.AI), AI, Hardware and Architecture, Software, 6G, Information Systems
الوصف: 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.
Comment: The article has been accepted for publication in IEEE Network
وصف الملف: fulltext
DOI: 10.48550/arxiv.2201.06947
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4e38af52f5aedd1ce483b29abecc03c
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....b4e38af52f5aedd1ce483b29abecc03c
قاعدة البيانات: OpenAIRE
الوصف
DOI:10.48550/arxiv.2201.06947