面向车辆多址接入边缘计算网络的任务协同计算迁移策略

乔冠华; 冷甦鹏; 刘浩; 黄开胜; 吴凡

doi:10.11959/j.issn.2096-3750.2019.00089

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面向车辆多址接入边缘计算网络的任务协同计算迁移策略

理论与技术 | 更新时间：2024-06-05

- 面向车辆多址接入边缘计算网络的任务协同计算迁移策略
- Task collaborative offloading scheme in vehicle multi-access edge computing network
- 物联网学报 2019年3卷第1期页码：51-59
- 作者机构：
  
  1. 电子科技大学信息与通信工程学院，四川成都 611731
  2. 北京市交通信息中心，北京 100073
  3. 清华大学汽车安全与节能国家重点实验室，北京 100084
- 作者简介：
  
  [ "乔冠华（1987- ），男，山西长治人，电子科技大学博士生，主要研究方向为下一代无线网络资源分配、移动边缘计算和存储技术。" ]
  [ "冷甦鹏（1973- ），男，四川资中人，电子科技大学教授、博士生导师，主要研究方向为物联网、车联网、新一代宽带无线网络、无线自组织网、智能交通信息系统的资源管理、介质访问控制、路由、组网与互联、智能算法理论及技术应用等。" ]
  [ "刘浩（1977- ），男，四川资中人，博士，北京市交通信息中心副主任，主要研究方向为智能交通技术、交通建模和交通仿真等。" ]
  [ "黄开胜（1970- ），男，广东丰顺人，博士，清华大学副研究员、博士生导师，主要研究方向为动力系统及智能网联车辆控制技术。" ]
  [ "吴凡（1978- ），男，四川成都人，博士，电子科技大学通信与信息工程学院副教授，主要研究方向为下一代无线网络资源分配、车联网网络技术和数能同传技术。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61374189);中央高校基本科研业务费资助项目;The Fundamental Research Funds for the Central Universities(ZYGX2016J001);教育部—中国移动联合基金资助项目;The Ministry of Education-China Mobile Joint Funding Project(MCM20160304)
- DOI：10.11959/j.issn.2096-3750.2019.00089
  中图分类号： TN929.53
- 纸质出版日期：2019-03-30，
  
  网络出版日期：2019-03，
- 稿件说明：
移动端阅览
乔冠华, 冷甦鹏, 刘浩, 等. 面向车辆多址接入边缘计算网络的任务协同计算迁移策略[J]. 物联网学报, 2019,3(1):51-59.

GUANHUA QIAO, SUPENG LENG, HAO LIU, et al. Task collaborative offloading scheme in vehicle multi-access edge computing network. [J]. Chinese journal on internet of things, 2019, 3(1): 51-59.
乔冠华, 冷甦鹏, 刘浩, 等. 面向车辆多址接入边缘计算网络的任务协同计算迁移策略[J]. 物联网学报, 2019,3(1):51-59. DOI： 10.11959/j.issn.2096-3750.2019.00089.

GUANHUA QIAO, SUPENG LENG, HAO LIU, et al. Task collaborative offloading scheme in vehicle multi-access edge computing network. [J]. Chinese journal on internet of things, 2019, 3(1): 51-59. DOI： 10.11959/j.issn.2096-3750.2019.00089.

摘要

为了解决传统移动边缘计算网络无法很好地支持车辆的高速移动性和动态网络拓扑，设计了车辆多址接入边缘计算网络，实现路边单元和智能车辆的协同计算迁移。在该网络架构下，提出了多址接入模式选择和任务分配的联合优化问题，旨在最大化系统的长期收益，同时满足多样化的车联网应用需求，兼顾系统的能量消耗。针对该复杂的联合优化问题，设计了基于深度增强学习的多址接入协同计算迁移策略，该策略能够很好地克服传统Q-learning算法因网络规模增加带来的维度灾难挑战。仿真结果验证了所提算法具有良好的计算性能。

Abstract

In order to solve the problem that traditional mobile edge computing network can’t be straightforwardly applied to the Internet of vehicles (IoV) due to high speed mobility and dynamic network topology

a vehicular edge multi-access computing network (VE-MACN) was introduced to realize collaborative computing offloading between roadside units and smart vehicles.In this context

the collaborative computation offloading was formulated as a joint multi-access model selection and task assignment problem to realize the good balance between long-term system utility

diverse needs of IoV applications and energy consumption.Considering the complex joint optimization problem

a deep reinforcement learning-based collaborative computing offloading scheme was designed to overcome the curse of dimensionality for Q-learning algorithm.The simulation results demonstrate that the feasibility and effectiveness of proposed offloading scheme.

关键词

移动边缘计算多址接入技术车联网计算迁移深度增强学习

Keywords

mobile edge computingmulti-access technologyInternet of vehicles (IoV)computation offloadingdeep reinforcement learning

references

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