A Survey on Resource Allocation in Vehicular Networks

Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, the authors present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. They discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.3019322
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Noor-A-Rahim, Md
  • Liu, Zilong
  • Lee, Haeyoung
  • Ali, G G Md Nawaz
  • Pesch, Dirk
  • Xiao, Pei
• Publication Date: 2022-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 701-721
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 2
  • 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: Autonomous vehicles; Connected vehicles; Intelligent transportation systems; Machine learning; Resource allocation; Technological innovations; Wireless communication systems
• Subject Areas: Data and Information Technology; Highways; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01837924
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 28 2022 9:53AM