Intelligent Task Offloading for Heterogeneous V2X Communications

With the rapid development of autonomous driving technologies, it becomes difficult to reconcile the conflict between ever-increasing demands for high process rate in the intelligent automotive tasks and resource-constrained on-board processors. Fortunately, vehicular edge computing (VEC) has been proposed to meet the pressing resource demands. Due to the delay-sensitive traits of automotive tasks, only a heterogeneous vehicular network with multiple access technologies may be able to handle these demanding challenges. In this article, the authors propose an intelligent task offloading framework in heterogeneous vehicular networks with three Vehicle-to-Everything (V2X) communication technologies, namely Dedicated Short Range Communication (DSRC), cellular-based V2X (C-V2X) communication, and millimeter wave (mmWave) communication. Based on stochastic network calculus, this article firstly derives the delay upper bounds of different offloading technologies with certain failure probabilities. Moreover, the authors propose a federated Q-learning method that optimally utilizes the available resources to minimize the communication/computing budgets and the offloading failure probabilities. Simulation results indicate that their proposed algorithm can significantly outperform the existing algorithms in terms of resource cost and offloading failure probability.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.3015210
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Xiong, Kai
  • Leng, Supeng
  • Huang, Chongwen
  • Yuen, Chau
  • Guan, Yong Liang
• Publication Date: 2021-4

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 2226-2238
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 4
  • 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: Algorithms; Computer network protocols; Connected vehicles; Data processing operations; Dedicated short range communications; Machine learning; Mobile computing; Vehicle to infrastructure communications
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01772857
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: May 25 2021 4:22PM