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

*この研究の対応する著者

研究成果: Article査読

4 被引用数 (Scopus)

抄録

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).

本文言語English
ページ(範囲)2547-2565
ページ数19
ジャーナルWorld Wide Web
23
4
DOI
出版ステータスPublished - 2020 7月 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信

フィンガープリント

「Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル