Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN

Tianpeng Ye, Xiang Lin*, Jun Wu, Gaolei Li, Jianhua Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The Fog Computing was proposed to extend the computing task to the network edge in lots of Internet of Things (IoT) scenario, such as Internet of Vehicle (IoV). However, the unbalanced data processing requirement caused by the uneven distribution of vehicles in time and space limits the service capability of IoV. To enhance the flexibility and data processing capability, we propose a hybrid fog architecture which composed by fog computing radio access network (F-RAN) and Vehicular Fog Computing (VFC), which is called VF-based F-RAN. In addition, we propose a heuristic algorithm enhanced by deep learning to optimize the computation offloading in this hybrid architecture. The simulation result reveals that the proposed hybrid fog architecture with the heuristic algorithm can effectively improve the data processing efficiency and balance the Quality of Experience (QoE).

Original languageEnglish
Pages (from-to)2547-2565
Number of pages19
JournalWorld Wide Web
Volume23
Issue number4
DOIs
Publication statusPublished - 2020 Jul 1
Externally publishedYes

Keywords

  • Computation offloading
  • Deep learning
  • F-RAN
  • Heuristic algorithm
  • Vehicular fog

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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