计算机科学技术学报 ›› 2019,Vol. 34 ›› Issue (4): 869-886.doi: 10.1007/s11390-019-1947-3

所属专题： 综述； Computer Architecture and Systems； Computer Networks and Distributed Computing

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车辆环境中边缘运算的应用：对比研究和主要问题

Leo Mendiboure1, Student Member, IEEE, Mohamed-Aymen Chalouf2, Francine Krief3   

  1. 1 LaBRI Laboratory, University of Bordeaux, Talence 33400, France;
    2 IRISA Laboratory, University of Rennes 1, Lannion 22300, France;
    3 LaBRI Laboratory, Bordeaux INP, Talence 33400, France
  • 收稿日期:2018-08-20 修回日期:2019-05-15 出版日期:2019-07-11 发布日期:2019-07-11
  • 作者简介:Leo Mendiboure is a Ph.D.student at the University of Bordeaux,Talence,working under the supervision of Prof.Francine Krief and Dr.Mohamed-Aymen Chalouf.He received his Master's degree in telecommunications engineering,from the ENSEIRB-MATMECA School of Engineers,Bordeaux,in October 2017.Since 2018,he is member of CNRS LaBRI Laboratory,UMR 5800, "Programming,Networks and Systems" team.His main research interests include vehicular networks,virtualization and management of Quality of Service.

Edge Computing Based Applications in Vehicular Environments: Comparative Study and Main Issues

Leo Mendiboure1, Student Member, IEEE, Mohamed-Aymen Chalouf2, Francine Krief3   

  1. 1 LaBRI Laboratory, University of Bordeaux, Talence 33400, France;
    2 IRISA Laboratory, University of Rennes 1, Lannion 22300, France;
    3 LaBRI Laboratory, Bordeaux INP, Talence 33400, France
  • Received:2018-08-20 Revised:2019-05-15 Online:2019-07-11 Published:2019-07-11

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尽管付出了极大的努力，车载自组织网络（VANETs）仍然面临许多问题，诸如：网络性能，网络可扩展性和场景感知。为了克服这些问题，提出了很多解决方案，其中，作为云计算的扩展的边缘计算就是其一。使用边缘运算，通信，存储和计算能力拉近了终端用户之间的距离。这能给全球车辆网络带来很多好处，例如，低延迟，网络卸载和场景感知（地点，环境因素等等）。已有很多边缘计算方法，例如：移动边缘运算（MEC），雾计算（FC）和微云计算（cloudlet）。介绍车辆环境背景之后，本文旨在研究和对比这些不同的技术。具体地，本文分析了它们的主要特征，以及在VANETs中最先进的应用。此外，对MEC，FC和微云运算进行分类，并且探讨了对于不同类型的车辆应用而言，它们的适用水平。最后，我们讨论了边缘计算和VANET领域的一些挑战及其未来的发展方向。

关键词: 云计算, 边缘计算, 雾计算, 微云计算, 车辆网络

Abstract: Despite the expanded efforts, the vehicular ad-hoc networks (VANETs) are still facing many challenges such as network performances, network scalability and context-awareness. Many solutions have been proposed to overcome these obstacles, and the edge computing, an extension of the cloud computing, is one of them. With edge computing, communication, storage and computational capabilities are brought closer to end users. This could offer many benefits to the global vehicular network including, for example, lower latency, network off-loading and context-awareness (location, environment factors, etc.). Different approaches of edge computing have been developed:mobile edge computing (MEC), fog computing (FC) and cloudlet are the main ones. After introducing the vehicular environment background, this paper aims to study and compare these different technologies. For that purpose their main features are compared and the state-ofthe-art applications in VANETs are analyzed. In addition, MEC, FC, and cloudlet are classified and their suitability level is debated for different types of vehicular applications. Finally, some challenges and future research directions in the fields of edge computing and VANETs are discussed.

Key words: cloud computing, edge computing, fog computing, cloudlet, vehicular network

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