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作者:

Zhang, Dajun (Zhang, Dajun.) | Shi, Wei (Shi, Wei.) | Yang, Ruizhe (Yang, Ruizhe.)

收录:

CPCI-S EI Scopus

摘要:

Recently, effective allocation of VANET resources is a key factor in promoting the development of VANETs. Due to high bandwidth costs, poor time efficiency, and a high risk of privacy leakage, the use of traditional centralized data centers to analyze massive data has proven to be a difficult task. These challenges have prompted a revolutionary change in VANET architectures to scatter computations from a centralized data center to distributed network edges. Distributed VANET configurations leverage the computing power of network edges by using a large number of mobile devices which frequently exchange data with the edge of the network or among themselves. However, the heterogeneity and distrust of the distributed edge hinder the efficient, reliable, and secure allocation of VANET resources. In this paper, we express the allocation strategy for both computing and network resources as a joint optimization problem. We use a local deep reinforcement learning with a prioritized experience replay mechanism on edge nodes and use the blockchain for sharing the optimal learning results to optimize the overall resource allocation problem. Simulation results show that our proposed scheme is superior to a current machine learning approach.

关键词:

Blockchain Vehicular ad hoc networks Deep reinforcement learning

作者机构:

  • [ 1 ] [Zhang, Dajun]Carleton Univ, 1125 Colonel By Dr, Ottawa, ON, Canada
  • [ 2 ] [Shi, Wei]Carleton Univ, 1125 Colonel By Dr, Ottawa, ON, Canada
  • [ 3 ] [Yang, Ruizhe]Beijing Univ Technol, Beijing Lab Adv Informat Networks, Beijing, Peoples R China

通讯作者信息:

  • [Zhang, Dajun]Carleton Univ, 1125 Colonel By Dr, Ottawa, ON, Canada

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来源 :

GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2022

ISSN: 0302-9743

年份: 2023

卷: 13744

页码: 110-121

被引次数:

WoS核心集被引频次: 1

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

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