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Data driven service orchestration for vehicular networks

Author
Dalgkitsis, A.; Mekikis, P.; Antonopoulos, A.; Verikoukis, C.
Type of activity
Journal article
Journal
IEEE transactions on intelligent transportation systems
Date of publication
2021-07
Volume
22
Number
7
First page
4100
Last page
4109
DOI
10.1109/TITS.2020.3011264
Project funding
Single Point of attachment communications heterogeneous mobile data networks (TEC2017-87456-P)
URL
https://ieeexplore.ieee.org/document/9153150 Open in new window
Abstract
As technology progresses, cars can not only be considered as a transportation medium but also as an intelligent part of the cellular network that generates highly valuable data and offers both entertainment and security services to the passengers. Therefore, forthcoming 5G networks are said to enhance Ultra-Reliable Ultra-Low-Latency that will allow for a new breed of services that will disrupt the industry as we know it today. In this work, we devise a unique fusion of Deep Learning based mobil...
Keywords
5G wireless mobile networks, Deep learning, Intelligent automotive, Mobility prediction, Proactive resource allocation, Vehicular networks

Participants

  • Dalgkitsis, Anestis  (author)
  • Mekikis, Prodromos-Vasileios  (author)
  • Antonopoulos, Angelos  (author)
  • Verikoukis, Christos  (author)