Hyppää sisältöön
    • Suomeksi
    • In English
Trepo
  • Suomeksi
  • In English
  • Kirjaudu
Näytä viite 
  •   Etusivu
  • Trepo
  • TUNICRIS-julkaisut
  • Näytä viite
  •   Etusivu
  • Trepo
  • TUNICRIS-julkaisut
  • Näytä viite
JavaScript is disabled for your browser. Some features of this site may not work without it.

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

Saafi, Salwa; Vikhrova, Olga; Fodor, Gabor; Hosek, Jiri; Andreev, Sergey (2022)

 
Avaa tiedosto
AI_Aided_Integrated_Terrestrial.pdf (1.931Mt)
Lataukset: 



Saafi, Salwa
Vikhrova, Olga
Fodor, Gabor
Hosek, Jiri
Andreev, Sergey
2022

IEEE Network
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
doi:10.1109/MNET.104.2100351
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202210117554

Kuvaus

Peer reviewed
Tiivistelmä
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.
Kokoelmat
  • TUNICRIS-julkaisut [23480]
Kalevantie 5
PL 617
33014 Tampereen yliopisto
oa[@]tuni.fi | Tietosuoja | Saavutettavuusseloste
 

 

Selaa kokoelmaa

TekijätNimekkeetTiedekunta (2019 -)Tiedekunta (- 2018)Tutkinto-ohjelmat ja opintosuunnatAvainsanatJulkaisuajatKokoelmat

Omat tiedot

Kirjaudu sisäänRekisteröidy
Kalevantie 5
PL 617
33014 Tampereen yliopisto
oa[@]tuni.fi | Tietosuoja | Saavutettavuusseloste