Joint Optimization Strategy of Computing Offloading and Edge Caching for Intelligent Connected Vehicles

doi:10.16182/j.issn1004731x.joss.22-0147

Abstract

Abstract:

To guarantee the low-delay communication of intelligent connected vehicles, the V2X channel model and the multi-access edge computing (MEC) technology, are used to carry out the research of the joint optimization strategy of computing offloading and edge caching. An intelligent connected vehicle with task offloading and edge caching model least-deep deterministic policy gradient(L-DDPG) is developed. By integrating the vehicular local and edge computing resources, the classification processing of different computing tasks in V2X scenarios is supported. The vehicular computing request is prejudged by edge platform to ensure the rapid response of continuous homogeneous computing tasks. Combining with the least recently used strategy, the new computing tasks are efficiently managed. A joint offloading decision for computing offloading and edge caching is carried out based on deep deterministic policy gradient(DDPG) algorithm. Simulation results show that the performance of L-DDPG model is better than that of traditional models, which can effectively improve the system performance, ensure the service quality, and reduce the time delay and resource consumption.

Key words: intelligent connected vehicle, vehicle to everything(V2X), multi-access edge computing, deep reinforcement learning, computing offloading, edge caching

CLC Number:

TP393

Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang. Joint Optimization Strategy of Computing Offloading and Edge Caching for Intelligent Connected Vehicles[J]. Journal of System Simulation, 2023, 35(6): 1203-1214.

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Table 1

Simulation parameters

参数	数值
RSU覆盖半径/m	300
车辆发射功率 $p v / d B m$	20
CPU能耗功率 $p c / W$	5
路侧边缘节点数	[3, 7]
单个MEC节点的计算能力 $f i m / G H z$	[5, 9]
上行传输信道数量 $K$	10
信道总带宽 $B / M H z$	20
高斯白噪声功率 $σ 2 / d B m$	-100
车辆计算能力 $f i v / G H z$	1
计算任务大小 $d i / M B$	20
权重因子 $α$	0.5
缓存空间 $H m / M B$	500
最大容忍时延 $t i m a x / s$	0.003
载波频率 $f c / G H z$	5.915
基站的高度 $h B S / m$	35
车辆的高度 $h U T / m$	1.5
发送信号功率 $P t x / d B m$	23
天线增益 $G a n t e n n a / d B i$	3
噪声功率 $P n o i s e / W$	$3 × 10 - 13$

Table 1

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

References 29

1	王君, 纪晓东, 张欣然, 等. 5G蜂窝车联网组网性能研究[J]. 电信科学, 2020, 36(1): 49-57.
	Wang Jun, Ji Xiaodong, Zhang Xinran, et al. Performance Evaluation of 5G Cellular Vehicle Networks[J]. Telecommunications Science, 2020, 36(1): 49-57.
2	European Telecommunications Standards Institute. ETSI GS MEC 002, Mobile Edge Computing(MEC); Technical Requirements: V1.1.1 [S]. Valbonne-sophia Antipolis France: European Telecommunications Standards Institute, 2016.
3	European Telecommunications Standards Institute. ETSI GS MEC 001, Mobile Edge Computing(MEC); Terminology: V1.1.1 [S]. Valbonne-sophia Antipolis France: European Telecommunications Standards Institute, 2016.
4	European Telecommunications Standards Institute. ETSI GS MEC-IEG 004, Mobile Edge Computing(MEC); Service Scenarios: V1.1.1 [S]. Valbonne-Sophia Antipolis France: European Telecommunications Standards Institute, 2015.
5	European Telecommunications Standards Institute. ETSI GS MEC 009, Mobile Edge Computing (MEC); General principles for Mobile Edge Service APIs: V1.1.1 [S]. Valbonne-sophia Antipolis France: European Telecommunications Standards Institute, 2017.
6	European Telecommunications Standards Institute. ETSI GS MEC 011, Mobile Edge Computing(MEC); Mobile Edge Platform Application Enablement: V1.1.1 [S]. Valbonne-sophia Antipolis France: European Telecommunications Standards Institute, 2017.
7	Zhang Ke, Mao Yuming, Leng Supeng, et al. Optimal Delay Constrained Offloading for Vehicular Edge Computing Networks[C]//2017 IEEE International Conference on Communications(ICC). Piscataway, NJ, USA: IEEE, 2017: 1-6.
8	Liu Yujiong, Wang Shangguang, Huang Jie, et al. A Computation Offloading Algorithm Based on Game Theory for Vehicular Edge Networks[C]//2018 IEEE International Conference on Communications(ICC). Piscataway, NJ, USA: IEEE, 2018: 1-6.
9	Wang Hansong, Li Xi, Ji Hong, et al. Federated Offloading Scheme to Minimize Latency in MEC-Enabled Vehicular Networks[C]//2018 IEEE Globecom Workshops(GC Wkshps). Piscataway, NJ, USA: IEEE, 2018: 1-6.
10	张海波, 荆昆仑, 刘开健, 等. 车联网中一种基于软件定义网络与移动边缘计算的卸载策略[J]. 电子与信息学报, 2020, 42(3): 645-652.
	Zhang Haibo, Jing Kunlun, Liu Kaijian, et al. An Offloading Mechanism Based on Software Defined Network and Mobile Edge Computing in Vehicular Networks[J]. Journal of Electronics & Information Technology, 2020, 42(3): 645-652.
11	张海波, 王子心, 贺晓帆. SDN和MEC架构下V2X卸载与资源分配[J]. 通信学报, 2020, 41(1): 114-124.
	Zhang Haibo, Wang zi Xin, He Xiaofan. V2X Offloading and Resource Allocation Under SDN and MEC Architecture[J]. Journal on Communications, 2020, 41(1): 114-124.
12	Huang Xumin, Yu Rong, Kang Jiawen, et al. Distributed Reputation Management for Secure and Efficient Vehicular Edge Computing and Networks[J]. IEEE Access, 2017, 5: 25408-25420.
13	黄永明, 郑冲, 张征明, 等. 大规模无线通信网络移动边缘计算和缓存研究[J]. 通信学报, 2021, 42(4): 44-61.
	Huang Yongming, Zheng Chong, Zhang Zhengming, et al. Research on Mobile Edge Computing and Caching in Massive Wireless Communication Network[J]. Journal on Communications, 2021, 42(4): 44-61.
14	Ji Hongjing, Alfarraj O, Tolba A. Artificial Intelligence-empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications[J]. IEEE Access, 2020, 8: 61020-61034.
15	兰巨龙, 王鹏, 申涓, 等. 大规模接入汇聚网络的抵近式缓存技术研究[J]. 物联网学报, 2017, 1(1): 50-54.
	Lan Julong, Wang Peng, Shen Juan, et al. Research on Push Cache of Large-scale Converging Access Networks[J]. Chinese Journal on Internet of Things, 2017, 1(1): 50-54.
16	El Haber E, Nguyen T M, Assi C. Joint Optimization of Computational Cost and Devices Energy for Task Offloading in Multi-tier Edge-clouds[J]. IEEE Transactions on Communications, 2019, 67(5): 3407-3421.
17	Zhang Ni, Guo Songtao, Dong Yifan, et al. Joint Task Offloading and Data Caching in Mobile Edge Computing Networks[J]. Computer Networks, 2020, 182: 107446.
18	Ko S W, Han Kaifeng, Huang Kaibin. Wireless Networks for Mobile Edge Computing: Spatial Modeling and Latency Analysis[J]. IEEE Transactions on Wireless Communications, 2018, 17(8): 5225-5240.
19	Ebrahimzadeh A, Maier M. Cooperative Computation Offloading in FiWi Enhanced 4G HetNets Using Self-organizing MEC[J]. IEEE Transactions on Wireless Communications, 2020, 19(7): 4480-4493.
20	Deng Maofei, Tian Hui, Xinchen Lü. Adaptive Sequential Offloading Game for Multi-cell Mobile Edge Computing[C]//2016 23rd International Conference on Telecommunications(ICT). Piscataway, NJ, USA: IEEE, 2016: 1-5.
21	Li Yongfu, Chen Wenbo, Peeta S, et al. Platoon Control of Connected Multi-vehicle Systems under V2X Communications: Design and Experiments[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 1891-1902.
22	The Alliance for Telecommunications Industry Solutions. ATIS.3GPP.37.885.V1530, Study on Evaluation Methodology of New Vehicle-to-everything (V2X) Use Cases for LTE and NR [S]. Washington, DC, US: ATIS, 2019.
23	3rd Generation Partnership Project. TR 38.901, Study on Channel Model for Frequencies from 0.5 to 100 GHz: V14.0.0 [S]. Valbonne-sophia Antipolis France: 3rd Generation Partnership Project, 2017.
24	刘雷, 陈晨, 冯杰, 等. 车载边缘计算中任务卸载和服务缓存的联合智能优化[J]. 通信学报, 2021, 42(1): 18-26.
	Liu Lei, Chen Chen, Feng Jie, et al. Joint Intelligent Optimization of Task Offloading and Service Caching for Vehicular Edge Computing[J]. Journal on Communications, 2021, 42(1): 18-26.
25	Li S L, Du J B, Zhai D S, et al. Task Offloading, Load Balancing, and Resource Allocation in MEC Networks[J]. IET Communications, 2020, 14(9): 1451-1458.
26	Meng Hao, Chao Daichong, Huo Ru, et al. Deep Reinforcement Learning Based Delay-sensitive Task Scheduling and Resource Management Algorithm for Multi-User Mobile-edge Computing Systems[C]//Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence. New York, NY, USA: Association for Computing Machinery, 2019: 66-70.
27	Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous Control with Deep Reinforcement Learning[C]//4th International Conference on Learning Representations, ICLR 2016. New York, USA: ICLR, 2016: 1-14.
28	Ye Junhong, Zhang Yingjun. DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networks[J]. IEEE Transactions on Mobile Computing, 2020, 19(9): 2076-2087.
29	Tong Minglei, Wang Xiaoxiang, Wang Yulong, et al. Computation Offloading Scheme with D2D for MEC-enabled Cellular Networks[C]//2020 IEEE/CIC International Conference on Communications in China(ICCC Workshops). Piscataway, NJ, USA: IEEE, 2020: 111-116.

[1]	Jiang Ming, He Tao. Solving the Vehicle Routing Problem Based on Deep Reinforcement Learning [J]. Journal of System Simulation, 2025, 37(9): 2177-2187.
[2]	Ni Peilong, Mao Pengjun, Wang Ning, Yang Mengjie. Robot Path Planning Based on Improved A-DDQN Algorithm [J]. Journal of System Simulation, 2025, 37(9): 2420-2430.
[3]	Chen Zhen, Wu Zhuoyi, Zhang Lin. Research on Policy Representation in Deep Reinforcement Learning [J]. Journal of System Simulation, 2025, 37(7): 1753-1769.
[4]	Wu Guohua, Zeng Jiaheng, Wang Dezhi, Zheng Long, Zou Wei. A Quadrotor Trajectory Tracking Control Method Based on Deep Reinforcement Learning [J]. Journal of System Simulation, 2025, 37(5): 1169-1187.
[5]	Bai Zhenzu, Hou Yizhi, He Zhangming, Wei Juhui, Zhou Haiyin, Wang Jiongqi. Optimization of Dynamic Weapon Target Assignment Considering Random Disturbances [J]. Journal of System Simulation, 2025, 37(12): 2967-2980.
[6]	Zheng Jiayu, Mai Zhuxue, Chen Zheyi. Optimization of Service Caching and Computation Offloading in Digital Twin Cloud-edge Networks [J]. Journal of System Simulation, 2025, 37(11): 2741-2753.
[7]	Di Jian, Wan Xue, Jiang Limei. Evolutionary Reinforcement Learning Based on Elite Instruction and Random Search [J]. Journal of System Simulation, 2025, 37(11): 2877-2887.
[8]	Xu Zhongkai, Chu Chenyang, Xie Kai, Zhao Ruizhuo, Ke Wenjun. Optimization Dispatch Method for High-proportion Renewable Energy Power Systems Based on SC-PPO [J]. Journal of System Simulation, 2025, 37(10): 2511-2521.
[9]	Liang Xiuman, Liu Ziliang, Liu Zhendong. Path Planning of Improved RRT Algorithm Based on Deep Reinforcement Learning [J]. Journal of System Simulation, 2025, 37(10): 2578-2593.
[10]	Jiang Jiachen, Jia Zhengxuan, Xu Zhao, Lin Tingyu, Zhao Pengpeng, Ou Yiming. Decision Modeling and Solution Based on Game Adversarial Complex Systems [J]. Journal of System Simulation, 2025, 37(1): 66-78.
[11]	Qin Baoxin, Zhang Yuxiao, Wu Sirui, Cao Weichong, Li Zhan. Intelligent Optimization of Coal Terminal Unloading Scheduling Based on Improved D3QN Algorithm [J]. Journal of System Simulation, 2024, 36(3): 770-781.
[12]	Li Ming, Ye Wangzhong, Yan Jiehua. Path Planning of Desert Robot Based on Deep Reinforcement Learning [J]. Journal of System Simulation, 2024, 36(12): 2917-2925.
[13]	Zhang Yongfu, Liu Yang, Yuan He. A Method for Key Node Identification in Operational Target System Based on War Gaming [J]. Journal of System Simulation, 2024, 36(11): 2654-2661.
[14]	An Jing, Si Guangya, Zhang Lei. Strategy Optimization Method of Multi-dimension Projection Based on Deep Reinforcement Learning [J]. Journal of System Simulation, 2024, 36(1): 39-49.
[15]	Laiyi Yang, Jing Bi, Haitao Yuan. Intelligent Path Planning for Mobile Robots Based on SAC Algorithm [J]. Journal of System Simulation, 2023, 35(8): 1726-1736.