车载边缘计算研究综述

doi:10.11959/j.issn.1000-0801.2023262

Abstract

Abstract:

With the explosive growth of computation-intensive and latency-sensitive vehicular applications, the centralized cloud architecture was faced with high workload and task latency congestion.In order to ensure service quality, vehicular edge computing emerged.The computing paradigm enabled the migration of computational power and storage resources to edge servers or edge gateways that were closer to the data source.By performing real-time data processing and decision-making at the edge nodes, significant reduction in data transmission latency was achieved.Firstly, the basic concept of vehicular edge computing was introduced, followed by a review and classification of existing research.Finally, prospects and future research directions for vehicular edge computing were discussed.

Key words: mobile edge computing, Internet of vehicles, vehicular edge computing, task offloading

CLC Number:

TP391

Xuefei PENG, Aohui LIU. A survey of vehicular edge computing[J]. Telecommunications Science, 2023, 39(12): 19-28.

Figures/Tables 5

文献	协作方式	边缘节点	评估指标	贡献
文献[20]	端云	—	时延、能耗	将优化问题公式化为无限域半马尔可夫决策过程，利用迭代算法寻找最优方案
文献[21]	端边	多服务器	时延，能耗	提出了将边缘节点的放置优化问题描述成一个二进制整数线性规划问题，满足了不同V2X应用的QoS要求
文献[22]	端边云	多服务器	时延、能耗	提出云计算中心和边缘计算的协同式计算任务卸载架构
文献[23]	端边	多服务器	时延	引入软件定义网络辅助路由和控制协议，将 IoV 系统划分为两个独立的层，提出了一种联合优化卸载决策、卸载率和资源分配的方法，以最小化系统平均时延
文献[26]	端边	多服务器	能耗	同时考虑了计算能量成本和缓存能量成本问题，提出了一种基于深度确定性策略梯度算法来解决该优化问题
文献[27]	端边	多服务器	时延、能耗	设计了一种新颖的以V2X应用数据为中心的框架，最小化应用程序的总执行时间和成本
文献[28]	端边	多服务器	服务率	设计了一个分布式的服务调度机制，通过融合不同服务器的资源，确保实时的业务需求，最大限度提升服务率
文献[30]	端边	多服务器	时延、能耗	提出了低复杂度启发式贪婪算法，该算法以计算时延和成本函数为优化目标来选择每项任务成本最低的服务器
文献[32]	端边	多服务器	时延、能耗	开发了一种基于粒子群的启发式算法来解决优化问题
文献[36]	端边	多服务器	时延、能耗	提出了基于DQN的深度强化学习策略。优化状态（state）中的成本函数
文献[37]	端边	单服务器	缓存成本	提出了一种具有优先级的任务卸载和资源分配方案，以优化功率分配，在此基础上设计了DDPG算法
文献[38]	端边	多服务器	时延、可靠性	提出了一种面向车联网边缘计算的多目标强化学习策略，用于有效地减少系统总成本，包括时延和可靠性。该策略通过结合通信和计算资源分配，实现了在不同任务条件下平均约50%的总体系统成本降低
文献[43]	端边	多服务器	边缘节点服务率	将任务卸载子问题建模为精确势博弈，并提出了一种多智能体分布式深度确定性策略梯度来实现纳什均衡
文献[44]	端边	多服务器	服务器效益、能耗	将资源分配问题转化为一场博弈游戏，并提出一种去中心化的算法寻找纳什均衡
文献[45]	端边	多服务器	时延、能耗	将多用户卸载决策问题表述为非合作博弈，并提出了一种基于机器学习技术的全分布式计算卸载算法来寻找纳什均衡点

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