区块链赋能的车辆边缘网络任务卸载方法研究

doi:10.19665/j.issn1001-2400.20250301

Abstract

Abstract:

With the booming development of urban intelligent transportation and the Internet of Vehicles (IoV),the demand for computing and cache resources has been increasing thanks to computationally intensive applications such as autonomous driving,image and voice processing.Considering that in the traditional cloud computing architecture of the Internet of Vehicles,the cloud servers are located in the core of the network,resulting in large propagation delays and making it difficult to provide real-time and high-quality services for moving vehicles.This study aims to propose a research on the task offloading methodology for vehicular edge networks enabled by the blockchain (BTO-VEN).Through task offloading,the computing tasks generated by vehicles are dynamically allocated to edge computing servers or neighboring vehicles,thereby reducing the computational load and latency.First,edge computing (MEC) is integrated into the Internet of Vehicles to form a vehicular edge network (VEN),and MEC servers are deployed at the network edge to reduce data communication latency and improve the computing efficiency of task offloading.Then,a dual deep learning algorithm of prediction-reward-detection (PRD-DDQN) is designed,which combines deep neural networks and the Q-learning algorithm to carry out strategy training and learning.The PRD training process is used to estimate the cumulative rewards between two adjacent states and incorporate these prediction results into the algorithm training to achieve dynamic decision-making to adapt to changing environments.Finally,blockchain technology is integrated into the MEC servers,and the integrity and reliability of task offloading transactions are maintained through a private blockchain structure.Experimental results show that compared with the traditional MADDPG method,the proposed method has great advantages,with a 39.98% increase in cumulative rewards and a 20.09% reduction in energy consumption.

Key words: blockchain, edge computing, deep learning, vehicular edge computing, task offloading

CLC Number:

TP393.1

KANG Haiyan, LIU Xinxu, LI Yanfang. Research on the task offloading methodology for vehicular edge networks enabled by the blockchain[J].Journal of Xidian University, 2025, 52(3): 85-98.

Figures/Tables 11

References 25

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方案	应用环境					应用
方案	区块链	车联网	边缘计算	雾计算	云计算	服务端	客户端
文献[11]	√	√	√		√		√
文献[12]		√	√	√		√
文献[13]	√	√		√			√
文献[14]			√		√	√	√
文献[15]	√	√					√
文献[16]	√	√	√				√
文中	√	√	√			√	√

参数	含义
V	车辆集合
T	任务集合
E_N	边缘节点集合
B	区块集合
T_R	交易集合
x_vt	卸载决策的二进制变量
κ_v	车辆v的能量系数
f_vt	任务执行所需的CPU周期频率
q_vt	车辆v对任务t执行卸载所需的能耗
c_vt	车辆v中执行任务t所需的计算能力
δ_vt	车辆v中执行任务t的计算时间
τ_t	卸载任务t的传输时间

符号	参数	参数值
B	区块链节点数	6
α	签名生成和验证所需的计算周期/M	1
β	MAC生成和验证所需的计算周期/M	10
D_n	任务的数据大小范围/M	[0.05,4]
EPISODE	迭代回合数	1 000

	PRD-DDQN	MADDPG	DQN	ETD
MIN	15.139 56	18.793 62	21.575 65	38.423 18
MAX	35.121 58	43.858 98	48.635 47	39.340 31
MEAN	18.092 07	21.887 07	25.767 67	38.910 03
MEDIAN	16.686 00	19.839 06	23.486 93	38.907 51
Std.	3.376 87	4.432 67	5.132 93	0.194 10

Research on the task offloading methodology for vehicular edge networks enabled by the blockchain

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