车联网边缘智能：概念、架构、问题、实施和展望

doi:10.11959/j.issn.2096-3750.2023.00320

物联网学报 ›› 2023, Vol. 7 ›› Issue (1): 37-48.doi: 10.11959/j.issn.2096-3750.2023.00320

车联网边缘智能：概念、架构、问题、实施和展望

江恺¹, 曹越¹^,², 周欢³, 任学锋², 朱永东⁴, 林海¹

¹ 武汉大学，湖北武汉 430072
² 华砺智行（武汉）科技有限公司，湖北武汉 430056
³ 三峡大学，湖北宜昌 443002
⁴ 之江实验室，杭州浙江 310100

修回日期:2022-12-30 出版日期:2023-03-30 发布日期:2023-03-01
作者简介:江恺（1995- ），男，武汉大学博士生，主要研究方向为边缘智能、多智能体/深度强化学习、智能交通系统等
曹越（1984- ），男，博士，武汉大学教授、博士生导师，主要研究方向为安全防护、网络计算、交通控制等
周欢（1986- ），男，博士，三峡大学教授、博士生导师，主要研究方向为移动社交网络、移动数据卸载、车联网等
任学锋（1979- ），男，华砺智行（武汉）科技有限公司副总裁、新技术研究院院长，主要研究方向为智能网联汽车、智慧交通等
朱永东（1974- ），男，博士，之江实验室研究员，主要研究方向为未来网络与通信、物联网、车联网等
林海（1976- ），男，博士，武汉大学副教授，主要研究方向为网络安全、物联网等
基金资助:
湖北省国际科技合作计划项目(2022EHB002);教育部中国高校产学研创新基金支持项目(2021LDA07005);湖北省重大调研课题基金项目(2022KT03-2)

Edge intelligence empowered internet of vehicles: concept, framework, issues, implementation, and prospect

Kai JIANG¹, Yue CAO¹^,², Huan ZHOU³, Xuefeng REN², Yongdong ZHU⁴, Hai LIN¹

¹ Wuhan University, Wuhan 430072, China
² Huali SmartWays Technology Co., Ltd., Wuhan 430056, China
³ China Three Gorges University, Yichang 443002, China
⁴ Zhejiang Lab, Zhejiang 310100, China

Revised:2022-12-30 Online:2023-03-30 Published:2023-03-01
Supported by:
Hubei Province International Science and Technology Collaboration Program(2022EHB002);Ministry of Education China University Industry-University-Research Innovation Program(2021LDA07005);Hubei Province Major Consultancy Program(2022KT03-2)

摘要/Abstract

摘要：

作为一项新兴交叉学科领域，边缘智能通过将人工智能推送至靠近交通数据源侧，并利用边缘算力、存储资源及感知能力，在提供实时响应、智能化决策、网络自治的同时，赋能更加智能、高效的资源调配与处理机制，从而实现车联网从接入“管道化”向信息“智能化”使能平台的跨越。然而，当前边缘智能于车联网领域的成功实施仍处于起步阶段，迫切需要以更为广阔的视角对这一新兴领域进行全面综述。为此，面向车联网应用场景，首先介绍边缘智能的背景、概念及关键技术；然后，对车联网应用场景中基于边缘智能的服务类型进行整体概述，同时详细阐述边缘智能模型的部署和实施过程；最后，分析边缘智能于车联网中的关键开放性挑战，并探讨应对策略，以推动其潜在研究方向。

关键词: 人工智能, 车联网, 边缘智能

Abstract:

As an emerging inter discipline field, edge intelligence pushes AI to the side close to the traffic data source.Edge intelligence makes use of the computing power, storage resources, and perception ability of edge to provide a more intelligent and efficient resource allocation and processing mechanism while providing a real-time response, intelligent decision-making and network autonomy, realizing the critical leap for internet of vehicles from access “pipelining” to the intelligent enabling platform of information.However, the successful implementation of edge intelligence in internet of vehicles is still in its infancy, and there exists a demand for a comprehensive survey in this young field from a broader perspective.Based on this context of internet of vehicles, the background, concepts and key technologies of edge intelligence were introduced.Then, a holistic overview of service types based on internet of vehicles was taken, and the entire processes of model training and inference in edge intelligence were elaborated.Finally, to promote the potential research directions, the key open challenges of edge intelligence in the internet of vehicles were analyzed, and the coping strategies were discussed.

Key words: artificial intelligence, internet of vehicles, edge intelligence

中图分类号:

TP391

江恺, 曹越, 周欢, 任学锋, 朱永东, 林海. 车联网边缘智能：概念、架构、问题、实施和展望[J]. 物联网学报, 2023, 7(1): 37-48.

Kai JIANG, Yue CAO, Huan ZHOU, Xuefeng REN, Yongdong ZHU, Hai LIN. Edge intelligence empowered internet of vehicles: concept, framework, issues, implementation, and prospect[J]. Chinese Journal on Internet of Things, 2023, 7(1): 37-48.

图/表 7

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参考文献 49

[1]	JIAU M K , HUANG S C , HWANG J N ,et al. Multimedia services in cloud-based vehicular networks[J]. IEEE Intelligent Transportation Systems Magazine, 2015,7(3): 62-79.
[2]	PENG J K , FAN Y , YIN G D ,et al. Collaborative optimization of energy management strategy and adaptive cruise control based on deep reinforcement learning[J]. IEEE Transactions on Transportation Electrification, 2022.
[3]	HUANG J H , CUI H X , CHEN C . Cluster-based radio resource management in dynamic vehicular networks[J]. IEEE Access, 2022(10): 43562-43570.
[4]	KAIWARTYA O , ABDULLAH A H , CAO Y ,et al. Internet of vehicles:motivation,layered architecture,network model,challenges,and future aspects[J]. IEEE Access, 2016,4(2): 5356-5373.
[5]	ZHOU H , JIANG K , LIU X X ,et al. Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing[J]. IEEE Internet of Things Journal, 2022,9(2): 1517-1530.
[6]	刘婷婷, 杨晨阳, 索士强 ,等. 无线通信中的边缘智能[J]. 信号处理, 2020,36(11): 1789-1803.
	LIU T T , YANG C Y , SUO S Q ,et al. Edge intelligence for wireless communication[J]. Journal of Signal Processing, 2020,36(11): 1789-1803.
[7]	ABBAS N , ZHANG Y , TAHERKORDI A ,et al. Mobile edge computing:a survey[J]. IEEE Internet of Things Journal, 2018,5(1): 450-465.
[8]	TALEB T , SAMDANIS K , MADA B ,et al. On multi-access edge computing:a survey of the emerging 5G network edge cloud architecture and orchestration[J]. IEEE Communications Surveys ＆ Tutorials, 2017,19(3): 1657-1681.
[9]	TANG M , WONG V W S . Deep reinforcement learning for task offloading in mobile edge computing systems[J]. IEEE Transactions on Mobile Computing, 2022,21(6): 1985-1997.
[10]	JIANG W , FENG G , QIN S ,et al. Multi-agent reinforcement learning for efficient content caching in mobile D2D networks[J]. IEEE Transactions on Wireless Communications, 2019,18(3): 1610-1622.
[11]	LI X H , WANG X F , WAN P J ,et al. Hierarchical edge caching in device-to-device aided mobile networks:modeling,optimization,and design[J]. IEEE Journal on Selected Areas in Communications, 2018,36(8): 1768-1785.
[12]	JIANG K , ZHOU H , ZENG D Z ,et al. Multi-agent reinforcement learning for cooperative edge caching in Internet of vehicles[C]// Proceedings of 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems. Piscataway:IEEE Press, 2020: 455-463.
[13]	GU B , GAO L X , WANG X D ,et al. Privacy on the edge:customizable privacy-preserving context sharing in hierarchical edge computing[J]. IEEE Transactions on Network Science and Engineering, 2020,7(4): 2298-2309.
[14]	张彦, 张科, 曹佳钰 . 边缘智能驱动的车联网[J]. 物联网学报, 2018,2(4): 40-48.
	ZHANG Y , ZHANG K , CAO J Y . Internet of vehicles empowered by edge intelligence[J]. Chinese Journal on Internet of Things, 2018,2(4): 40-48.
[15]	XU X L , LI H Y , XU W J ,et al. Artificial intelligence for edge service optimization in internet of vehicles:a survey[J]. Tsinghua Science and Technology, 2022,27(2): 270-287.
[16]	ZHOU Z , CHEN X , LI E ,et al. Edge intelligence:paving the last mile of artificial intelligence with edge computing[J]. Proceedings of the IEEE, 2019,107(8): 1738-1762.
[17]	JIANG K , SUN C , ZHOU H ,et al. Intelligence-empowered mobile edge computing:framework,issues,implementation,and outlook[J]. IEEE Network, 2021,35(5): 74-82.
[18]	XU D L , LI T , LI Y ,et al. Edge intelligence:empowering intelligence to the edge of network[J]. Proceedings of the IEEE, 2021,109(11): 1778-1837.
[19]	WANG F X , ZHANG M , WANG X X ,et al. Deep learning for edge computing applications:a state-of-the-art survey[J]. IEEE Access, 2020,8(1): 58322-58336.
[20]	LUONG N C , HOANG D T , GONG S M ,et al. Applications of deep reinforcement learning in communications and networking:a survey[J]. IEEE Communications Surveys ＆ Tutorials, 2019,21(4): 3133-3174.
[21]	WANG F X , WANG F , LIU J C ,et al. Intelligent video caching at network edge:a multi-agent deep reinforcement learning approach[C]// Proceedings of IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2020: 2499-2508.
[22]	CHAHBAR M , DIAZ G , DANDOUSH A ,et al. A comprehensive survey on the E2E 5G network slicing model[J]. IEEE Transactions on Network and Service Management, 2021,18(1): 49-62.
[23]	SONG C , ZHANG M , ZHAN Y Y ,et al. Hierarchical edge cloud enabling network slicing for 5G optical fronthaul[J]. Journal of Optical Communications and Networking, 2019,11(4): B60-B70.
[24]	张朝昆, 崔勇, 唐翯翯 ,等. 软件定义网络（SDN）研究进展[J]. 软件学报, 2015,26(1): 62-81.
	ZHANG C K , CUI Y , TANG H H ,et al. State-of-the-art survey on software-defined networking(SDN)[J]. Journal of Software, 2015,26(1): 62-81.
[25]	HAN K , LI S R , TANG S F ,et al. Application-driven end-to-end slicing:when wireless network virtualization orchestrates with NFV-based mobile edge computing[J]. IEEE Access, 2018,6(1): 26567-26577.
[26]	ZHANG J , LETAIEF K B . Mobile edge intelligence and computing for the internet of vehicles[J]. Proceedings of the IEEE, 2020,108(2): 246-261.
[27]	MAO S , LENG S P , ZHANG Y . Joint communication and computation resource optimization for NOMA-assisted mobile edge computing[C]// Proceedings of ICC 2019 - 2019 IEEE International Conference on Communications. Piscataway:IEEE Press, 2019: 1-6.
[28]	ZHANG K , MAO Y M , LENG S P ,et al. Optimal delay constrained offloading for vehicular edge computing networks[C]// Proceedings of 2017 IEEE International Conference on Communications. Piscataway:IEEE Press, 2018: 1-6.
[29]	CHANG Z , LIU L Q , GUO X J ,et al. Dynamic resource allocation and computation offloading for IoT fog computing system[J]. IEEE Transactions on Industrial Informatics, 2021,17(5): 3348-3357.
[30]	NING Z L , DONG P R , KONG X J ,et al. A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of Things[J]. IEEE Internet of Things Journal, 2019,6(3): 4804-4814.
[31]	WANG J F , LV T J , HUANG P M ,et al. Mobility-aware partial computation offloading in vehicular networks:a deep reinforcement learning based scheme[J]. China Communications, 2020,17(10): 31-49.
[32]	ZHOU H , WU T , ZHANG H J ,et al. Incentive-driven deep reinforcement learning for content caching and D2D offloading[J]. IEEE Journal on Selected Areas in Communications, 2021,39(8): 2445-2460.
[33]	QIAN L P , WU Y , JIANG F L ,et al. NOMA assisted multi-task multi-access mobile edge computing via deep reinforcement learning for industrial Internet of Things[J]. IEEE Transactions on Industrial Informatics, 2021,17(8): 5688-5698.
[34]	YAN J , BI S Z , ZHANG Y J A . Offloading and resource allocation with general task graph in mobile edge computing:a deep reinforcement learning approach[J]. IEEE Transactions on Wireless Communications, 2020,19(8): 5404-5419.
[35]	TAN L T , HU R Q . Mobility-aware edge caching and computing in vehicle networks:a deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018,67(11): 10190-10203.
[36]	ALNAGAR Y , GOHARY R H , HOSNY S ,et al. Mobility-aware edge caching for minimizing latency in vehicular networks[J]. IEEE Open Journal of Vehicular Technology, 2022(3): 68-84.
[37]	AO W C , PSOUNIS K . Fast content delivery via distributed caching and small cell cooperation[J]. IEEE Transactions on Mobile Computing, 2018,17(5): 1048-1061.
[38]	WU H J , ZHANG J , CAI Z P ,et al. Toward energy-aware caching for intelligent connected vehicles[J]. IEEE Internet of Things Journal, 2020,7(9): 8157-8166.
[39]	WANG X F , CHEN M , TALEB T ,et al. Cache in the air:exploiting content caching and delivery techniques for 5G systems[J]. IEEE Communications Magazine, 2014,52(2): 131-139.
[40]	QIAO G H , LENG S P , MAHARJAN S ,et al. Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks[J]. IEEE Internet of Things Journal, 2020,7(1): 247-257.
[41]	WANG X F , WANG C Y , LI X H ,et al. Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching[J]. IEEE Internet of Things Journal, 2020,7(10): 9441-9455.
[42]	张星洲, 鲁思迪, 施巍松 . 边缘智能中的协同计算技术研究[J]. 人工智能, 2019,6(5): 55-67.
	ZHANG X Z , LU S D , SHI W S . Research on cooperative computing technologies in edge intelligence[J]. AI-View, 2019,6(5): 55-67.
[43]	KHAN R , KUMAR P , JAYAKODY D N K ,et al. A survey on security and privacy of 5G technologies:potential solutions,recent advancements,and future directions[J]. IEEE Communications Surveys ＆ Tutorials, 2020,22(1): 196-248.
[44]	李克强, 常雪阳, 李家文 ,等. 智能网联汽车云控系统及其实现[J]. 汽车工程, 2020,42(12): 1595-1605.
	LI K Q , CHANG X Y , LI J W ,et al. Cloud control system for intelligent and connected vehicles and its application[J]. Automotive Engineering, 2020,42(12): 1595-1605.
[45]	NAN K M , LIU S C , DU J Z ,et al. Deep model compression for mobile platforms:a survey[J]. Tsinghua Science and Technology, 2019,24(6): 677-693.
[46]	XU M T , ALAMRO S , LAN T ,et al. CRED:cloud right-sizing with execution deadlines and data locality[J]. IEEE Transactions on Parallel and Distributed Systems, 2017,28(12): 3389-3400.
[47]	TAYLOR B , MARCO V S , WOLFF W ,et al. Adaptive deep learning model selection on embedded systems[C]// Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages,Compilers,and Tools for Embedded Systems. New York:ACM Press, 2018,53(6): 31-43.
[48]	KANG Y P , HAUSWALD J , GAO C ,et al. Neurosurgeon[J]. ACM SIGARCH Computer Architecture News, 2017,45(1): 615-629.
[49]	KARSAVURAN M O , ACER S , AYKANAT C . Partitioning models for general medium-grain parallel sparse tensor decomposition[J]. IEEE Transactions on Parallel and Distributed Systems, 2021,32(1): 147-159.

模型训练	模型推断	云（中心节点）	路侧单元
集中式	集中式	训练 + 推断	N/A
集中式（共享模型）	分布式	训练	推断
分布式	分布式	N/A	训练 + 推断

车联网边缘智能：概念、架构、问题、实施和展望

Edge intelligence empowered internet of vehicles: concept, framework, issues, implementation, and prospect

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