Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm

Yuliang CONG; Wenxi SUN; Ke XUE; Zhihong QIAN; Mianshu CHEN

doi:10.11959/j.issn.1000-436x.2022188

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Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm

Papers | 更新时间：2024-06-05

- Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm
- Journal on Communications Vol. 43, Issue 10, Pages: 77-85(2022)
- 作者机构：
  
  1. 吉林大学通信工程学院，吉林长春 130012
  2. 中国科学院长春光学精密机械与物理研究所，吉林长春 130012
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61771219)
- DOI：10.11959/j.issn.1000-436x.2022188
  CLC： TN929.5
- Online First：2022-10，
  
  Published：25 October 2022
- 稿件说明：
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Yuliang CONG, Wenxi SUN, Ke XUE, et al. Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm[J]. Journal on Communications, 2022, 43(10): 77-85.
DOI：

Yuliang CONG, Wenxi SUN, Ke XUE, et al. Research on task offloading strategy of Internet of vehicles based on improved hybrid genetic algorithm[J]. Journal on Communications, 2022, 43(10): 77-85. DOI： 10.11959/j.issn.1000-436x.2022188.

摘要

摘要：针对多车多服务器车联网场景下的卸载决策造成资源分配不合理的问题，提出了一种两阶段启发式的车联网任务卸载策略。该策略利用改进的混合遗传算法（IGHA）与改进的人工鱼群算法（AFSA），结合系统对其内部的平均开销、时延与能耗的要求，对2种改进算法进行多次迭代求解，在任务卸载过程中实现最优资源分配。仿真实验结果表明，相比于基准方案，所提方案可以有效地降低系统内开销并提高任务卸载效率。

Abstract

Aiming at the problem of unreasonable resource allocation caused by the unloading decision in the multi-vehicle and multi-server IoV scenario

a two-stage heuristic IoV task offloading strategy was proposed.This strategy used the improved hybrid genetic algorithm (IHGA) and the improved artificial fish swarm algorithm (AFSA)

combined with the system’s internal average overhead

delay and energy consumption requirements

the two improved algorithm was used for multiple iterations to achieve optimal resource allocation in the process of task unloading.The simulation results show that the proposed scheme can effectively reduce the system internal overhead and improve the task offloading efficiency compared with the benchmark scheme.

关键词

Keywords

references

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