A Belief-Based Task Offloading Algorithm in Vehicular Edge Computing

Haneul Ko, Joonwoo Kim, Dongkyun Ryoo, Inho Cha, Sangheon Pack

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In vehicular edge computing (VEC), where vehicles offload their tasks to nearby edge clouds, it is not a trivial issue to design an optimal task offloading policy due to the dynamic nature of VEC environment and limited information on computing and communication resources. In this paper, we propose a belief-based task offloading algorithm (BTOA) where a vehicle selects target edge clouds (for computing) and subchannels (for communications) based on its belief, and observe their current resource and channel conditions. Based on the observed information, the vehicle finally determines the most appropriate edge cloud and subchannel. Evaluation results under a realistic traffic scenario demonstrate that BTOA can reduce the total latency of the task offloading over 42% compared to a conventional offloading algorithm where the target edge clouds and subchannels are determined without any real observations.

Original languageEnglish
Pages (from-to)5467-5476
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number5
DOIs
Publication statusPublished - 2023 May 1
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

Keywords

  • POMDP
  • Vehicular edge computing
  • belief vector
  • cloud
  • task offloading

ASJC Scopus subject areas

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications

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