Data Driven Service Orchestration for Vehicular Networks

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 it is known today. In this work, the authors devise a unique fusion of Deep Learning based mobility prediction and Genetic Algorithm assisted service orchestration to retain the average service latency minimal by offering personalized service migration, while tightly packing as many services as possible in the edge of the network, for maximizing resource utilization. Through an extensive simulation based on real data, the authors evaluate the proposed mobility orchestration combination and find gains in low latency in all examined scenarios.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.3011264
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Dalgkitsis, Anestis
  • 0000-0001-6838-1768
  • Mekikis, Prodromos-Vasileios
  • 0000-0003-0616-0657
  • Antonopoulos, Angelos
  • 0000-0002-3546-1080
  • Verikoukis, Christos
  • 0000-0001-8774-1052
• Publication Date: 2021-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 4100-4109
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Data communications; Genetic algorithms; Simulation; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01786959
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 29 2021 3:40PM