AI算法在车联网通信与计算中的应用综述

doi:10.11959/j.issn.1000-0801.2023019

Abstract

Abstract:

In the 5G era, the development of communication and computing in the Internet of vehicles has been limited by the rapidly increasing amount of information.New breakthroughs in communication and computing in Internet of vehicles can be achieved by applying AI algorithms to the Internet of vehicles.Firstly, the application of AI algorithms in communication security, communication resource allocation, computation resource allocation, task offloading decision, server deployment, communication-computation integration were investigated.Secondly, the achievements and shortcomings of the present AI algorithms in different scenarios were analyzed.Finally, combined with the Internet of vehicle development trend, some future research directions for AI algorithms applied in the Internet of vehicles were discussed.

Key words: communication resource allocation, communication security, computation offloading, communication-computation integration, AI algorithm, deep reinforcement learning

CLC Number:

TN929

Yu KANG, Yaqiong LIU, Tongyu ZHAO, Guochu SHOU. A survey on AI algorithms applied in communication and computation in Internet of vehicles[J]. Telecommunications Science, 2023, 39(1): 1-19.

Figures/Tables 9

文献	AI算法	优化内容	贡献
文献[8]	Q-learning	密钥更新时延通信加/解密计算时延	提出了一种基于 Q-learning 的车联网群密钥分配管理技术，提升了群集的通信安全等级，降低了通信时延
文献[9]		复杂环境中保证高QoS	提出了一种基于 Q-learning 的网格路由协议，可以在可靠性和端到端时延方面提供较高的QoS性能
文献[10]	DQN	高QoS	提出了一种基于DQN的新的考虑车辆速度影响的动态业务迁移方案，可以提高QoS，实现更高的系统利用率
文献[11]		提高 V2V 安全包广播的性能	提出了一种基于竞争信息状态表示的自适应 MAC 层算法，并结合 DQN算法，提高了V2V安全包广播的性能
文献[12]		最小化接收信息时延	使用 DQN 算法，使 RSU 可以立即执行最优调度决策，建立一个达到可接受的QoS水平的绿色和安全的车辆网络
文献[13]		车联网中的网络负载、通信可靠性	提出了一种基于DQN的协同感知方案，减轻了车载网络中的网络负载，提高了通信可靠性
文献[14]	CNN-LSTM	入侵检测的准确性和低时延	提出Spark框架的CNN-LSTM的深度学习算法，可以很好地满足入侵检测对准确性和实时性的要求
文献[15]	反卷积网络	车路协同推断的隐私问题	提出 3 种基于差分隐私的防御算法，通过理论计算和实验证明所提算法可有效防御黑盒图像还原攻击，同时保持车路协同推断的精确度
文献[16]	MAPPO	区块验证的安全性与可靠性	将智能汽车和验证者之间的交互过程建模成斯坦伯格博弈模型，利用MAPPO算法求解，保证了区块验证安全性与可靠性
文献[17]	T-DDRL	车载自组网技术中最可信路由路径的选择	提出了一种T-DDRL方法，深度神经网络利用SDN控制器作为智能体可以学习最可信的路由路径
文献[18]	MARDPG-AG	车载网络的安全性	提出一种鲁棒的MARDPG-AG算法，增强了车载网络的安全性

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文献[70]	Q-learning	计算量	任务车辆	部分卸载	提出了基于Q-learning的通信与计算联合资源分配
		时延	MEC服务器		算法，缓解异构车联网架构下算量大、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
文献[71]		时延	任务车辆	全部卸载	在移动边缘计算层采用Q-learning实现通信和计算
		可靠性	MEC服务器		资源的分配的最优解，有效地降低了系统总成本
			云服务器
文献[72]	DQN	吞吐量	任务车辆		采用DQN算法优化波束宽度设计，使得在满足车
			MEC服务器		辆之间公平性的同时系统吞吐量最大
文献[73]		服务成本	任务车辆		提出了一种基于DQN的VEC网络协作数据调度方
			MEC服务器		案，该方法可以有效地优化数据调度，以最小化
			服务车辆		数据丢失
文献[74]			无人机		提出了一种基于DQN的无人机临时辅助VECN信
					道分配和任务卸载策略方案，无人机基于DQN算
					法选择最优任务处理策略
文献[75]	DDPG	能耗	任务车辆		利用DDPG，在MEC服务器上设计了一种实时自
		卸载性能	MEC节点		适应算法来分配计算资源和传输资源，提高了长
					期平均任务成功率和发射功率
文献[76]	SAC、PPO	服务成本	任务车辆		提出了DSDRL框架进行联合资源优化，可以满足
			MEC服务器		不同服务的不同需求，降低成本
			服务车辆
文献[77]	MADDPG	时延	MEC服务器		采用MADDPG对每个MEC服务器的决策方案进
			（在无人机上安装）		行优化，使每个MEC服务器实时进行车辆关联和
					资源分配

A survey on AI algorithms applied in communication and computation in Internet of vehicles

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