The Journal of China Universities of Posts and Telecommunications ›› 2019, Vol. 26 ›› Issue (6): 30-42.doi: 10.19682/j.cnki.1005-8885.2019.1024

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Research on adaptive dual task offloading decision algorithm for parking space recommendation service

Peng Weiping, Su Zhe, Song Cheng, Jia Zongpu   

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
  • Received:2019-04-23 Revised:2019-12-03 Online:2019-12-31 Published:2020-03-10
  • Contact: Peng Weiping, E-mail:
  • About author:Peng Weiping, E-mail:
  • Supported by:
    This work was supported by the Scientific Research Project of Henan Province (182102110333), the Doctoral Support Research Project of Henan Polytechnic University (B2012-050), the Funding Project for the Young Backbone Teachers of Higher Education Institutions in Henan Province (2019GGJS061).


In order to improve the efficiency of tasks processing and reduce the energy consumption of new energy vehicle (NEV), an adaptive dual task offloading decision-making scheme for Internet of vehicles is proposed based on information-assisted service of road side units (RSUs) and task offloading theory. Taking the roadside parking space recommendation service as the specific application Scenario, the task offloading model is built and a hierarchical self-organizing network model is constructed, which utilizes the computing power sharing among nodes, RSUs and mobile edge computing (MEC) servers. The task scheduling is performed through the adaptive task offloading decision algorithm, which helps to realize the available parking space recommendation service which is energy-saving and environmental-friendly. Compared with these traditional task offloading decisions, the proposed scheme takes less time and less energy in the whole process of tasks. Simulation results testified the effectiveness of the proposed scheme.

Key words:

parking space recommendation service, Internet of vehicles, RSU, adaptive dual task offloading

CLC Number: