A survey on vehicular task offloading: Classification, issues, and challenges

Manzoor Ahmed, Salman Raza, Muhammad Ayzed Mirza, Abdul Aziz, Manzoor Ahmed Khan, Wali Ullah Khan, Jianbo Li, Zhu Han

Research output: Contribution to journalReview articlepeer-review

8 Citations (Scopus)

Abstract

Emerging vehicular applications with strict latency and reliability requirements pose high computing requirements, and current vehicles’ computational resources are not adequate to meet these demands. In this scenario, vehicles can get help to process tasks from other resource-rich computing platforms, including nearby vehicles, fixed edge servers, and remote cloud servers. Nonetheless, different vehicular communication network (VCN) modes need to be utilized to access these computing resources, improving applications and networks’ performance and quality of service (QoS). In this paper, we present a comprehensive survey on the vehicular task offloading techniques under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). For the task/computation offloading, we present the classification of methods under the V2V, V2I, and V2X communication domains. Besides, the task/computation offloading categories are each sub-categorized according to their schemes’ objectives. Furthermore, the literature on vehicular task offloading is elaborated, compared, and analyzed from the perspectives of approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

Original languageEnglish
Pages (from-to)4135-4162
Number of pages28
JournalJournal of King Saud University - Computer and Information Sciences
Volume34
Issue number7
DOIs
Publication statusPublished - Jul 2022

Keywords

  • 802.11bd
  • Cloudlet
  • Edge
  • NR V2X
  • Vehicular task offloading
  • cloud

ASJC Scopus subject areas

  • Computer Science(all)

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