Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 383-386.doi: 10.11896/jsjkx.200900212

• Network & Communication • Previous Articles     Next Articles

Research on Mobile Edge Computing in Expressway

SONG Hai-ning1, JIAO Jian1, LIU Yong2   

  1. 1 School of Computer Science,Beijing Information Science &Technology University,Beijing 100101,China
    2 Bai Rong Yun Chuang Technology Company Limited,Beijing 100102,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:SONG Hai-ning,born in 1997,postgra-duate,is a member of China Computer Federation.Her main research interests include edge computing and network security.
    JIAO Jian,born in 1978,Ph.D,assistantprofessor,postgraduate supervisor,is a member of China Computer Federation.His main research interests include edge computing and network security.
  • Supported by:
    National Natural Science Foundation of China(61872044).

Abstract: With the improvement of expressway construction,a large number of computing devices appear on both sides of the expressway.Based on this,mobile edge computing technology can be used in expressway scenarios.Mobile edge computing can provide vehicles with low latency,high bandwidth and reliable computing services on highways.It is also an important means to rea-lize intelligent transportation system.Considering the special environment of expressway,this paper focuses on the problems of task offloading and resource allocation.Combined with 5G mobile network,a mobile edge computing model for expresswaydri-ving task is established.An efficient and reasonable resource scheduling strategy for different kinds of computing tasksis designed.And a dynamic scheduling strategy is put forward,which combines genetic algorithm and ant colony algorithm.To verify the effectiveness of this model,load balancing is used as the performance index for the simulation experiment.The experimental results show that the proposed algorithm can effectively reduce the load gaps and the computing cost compared with similar algorithms

Key words: Expressway, Load balancing, Mobile edge computing, Resource allocation, Task offloading

CLC Number: 

  • TN929.5
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