车联雾计算中的异构接入与资源分配算法研究

熊凯; 冷甦鹏; 张可; 刘浩

doi:10.11959/j.issn.2096-3750.2019.00108

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车联雾计算中的异构接入与资源分配算法研究

理论与技术 | 更新时间：2024-06-05

- 车联雾计算中的异构接入与资源分配算法研究
- Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing
- 物联网学报 2019年3卷第2期页码：20-27
- 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. 北京市交通运行监测调度中心，北京 100161
  3. 北京市交通信息中心，北京 100161
- 作者简介：
  
  [ "熊凯（1991-），男，四川巴中人，电子科技大学博士生，主要研究方向为车联网资源分配、移动边缘计算和机器学习。" ]
  [ "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。" ]
  [ "张可（1974-），男，河南新乡人，博士、研究员，北京市交通运行监测调度中心副主任，主要研究方向为智能交通技术研究与应用、北京市综合交通运行监测服务和运行分析。" ]
  [ "刘浩（1977- ），男，四川资中人，博士，北京市交通信息心副主任，主要研究方向为智能交通技术交通建模和交通仿真等。" ]
- 基金信息：
  
  国家重点研发项目;The National Key R＆D Program of China(2018YFC0807101);国家自然科学基金重点资助项目;The State Key Program of National Natural Science Foundation of China(61731006);四川省科技计划项目;Sichuan Province Science and Technology Program(2019YFH0007);欧盟地平线2020计划项目COSAFE;The European Union’s Horizon 2020 Research(MSCA-RISE-2018-824019)
- DOI：10.11959/j.issn.2096-3750.2019.00108
  中图分类号： TN929
- 纸质出版日期：2019-06-30，
  
  网络出版日期：2019-06，
- 稿件说明：
移动端阅览
熊凯, 冷甦鹏, 张可, 等. 车联雾计算中的异构接入与资源分配算法研究[J]. 物联网学报, 2019,3(2):20-27.

KAI XIONG, SUPENG LENG, KE ZHANG, et al. Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing. [J]. Chinese journal on internet of things, 2019, 3(2): 20-27.
熊凯, 冷甦鹏, 张可, 等. 车联雾计算中的异构接入与资源分配算法研究[J]. 物联网学报, 2019,3(2):20-27. DOI： 10.11959/j.issn.2096-3750.2019.00108.

KAI XIONG, SUPENG LENG, KE ZHANG, et al. Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing. [J]. Chinese journal on internet of things, 2019, 3(2): 20-27. DOI： 10.11959/j.issn.2096-3750.2019.00108.

摘要

随着智能交通的发展，自动驾驶、智能车载交互、安全预警等新型车载应用不断涌现，独立车辆依靠自身有限的计算资源难以运行这些种类繁多且具有大量计算需求和时延需求的应用。雾计算通过将计算任务分布在网络边缘的设备中，运用虚拟化、分布式计算和并行计算技术，使用户能够按需动态地获取计算能力、存储空间等服务。将雾计算架构应用于车联网能够有效缓解计算量大、低时延车载应用与车辆有限且不均的资源分布之间的矛盾。从分析车—车通信、车—基础设施通信以及车辆时延容忍网络通信的信道容量入手，建立车联网异构接入的多业务资源优化模型，通过联合调度各类车联雾资源，实现智能交通应用的高效处理。仿真结果表明，所提出的强化学习算法能够有效地应对异构车联雾架构下的资源优化。

Abstract

With the development of intelligent transportation and the constant emergence of new vehicular on-board applications

such as automatic driving

intelligent vehicular interaction and safety driving.It is difficult for an independent vehicle to run a wide variety of applications with a large number of computing needs and time delay needs relying on its own limited computing resources.By distributing computing tasks in devices on the edge of the network

fog computing applies virtualization technology

distributed computing technology and parallel computing technology to enable users to dynamically obtain computing power

storage space and other services on demand.Applying fog computing architecture to Internet of vehicles can effectively alleviate the contradiction between the large computing-low delay demands and limited vehicular resources.By analyzing the channel capacity of vehicle-to-vehicle communication

vehicle-infrastructure communication and vehicle-time-delay tolerant network communication

an optimization model of heterogeneous access to multi-service resources for the Internet of vehicles was established

and various vehicle-to-fog resources were jointly dispatched to realize efficient processing of intelligent transportation applications.The simulation results show that the proposed reinforcement learning algorithm can effectively deal with the resource allocation in the heterogeneous vehicular fog architecture.

关键词

车联网车联雾车辆时延容忍网络Q学习算法资源分配

Keywords

Internet of vehicles (IoV)vehicular fogvehicular delay tolerant network (VDTN)Q-learning algorithmresource allocation

references

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ZHANG H , QIU Y , CHU X ,et al. Fog radio access networks:mobility management,interference mitigation,and resource optimization[J]. IEEE Wireless Communications, 2017,24(6): 120-127.

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KUI X , SUN Y , ZHANG S ,et al. Characterizing the capability of vehicular fog computing in large-scale urban environment[J]. Mobile Networks and Applications, 2018,23(4): 1050-1067.

张海波, 栾秋季, 朱江 ,等. 车辆异构网中基于移动边缘计算的任务卸载与资源分配[J]. 物联网学报, 2018,2(3): 36-43.

ZHANG H B, LUAN Q J, ZHU J ,et al. Task offloading and resource allocation in vehicle heterogeneous networks with MEC[J]. Chinese Journal on Internet of Things, 2018,2(3): 36-43.

O'DONOGHUE B, MUNOS R, KAVUKCUOGLU K ,et al. Combining policy gradient and Q-learning[C]// International Conference on Learning Representations(ICIR), 2017.

张德干, 葛辉, 刘晓欢 ,等. 一种基于 Q-Learning 策略的自适应移动物联网路由新算法[J]. 电子学报, 2018,46(10): 2325-2332.

ZHANG D G, GE H, LIU X H ,et al. A new adaptive mobile Internet of things routing algorithm based on Q-learning strategy[J]. Acta Electronica Sinica, 2018,46(10): 2325-2332.

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XIONG K, LENG S P, HU J ,et al. Smart network slicing for vehicular fog-RAN[J]. IEEE Transactions on Vehicular Technology, 2019,68(4): 3075-3085.

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