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作者:

Zhang, Dajun (Zhang, Dajun.) | Yu, F. Richard (Yu, F. Richard.) | Yang, Ruizhe (Yang, Ruizhe.)

收录:

EI Scopus SCIE

摘要:

Vehicular ad hoc networks (VANETs) have become an important branch of future 6G smart wireless communications. As an emerging key technology, multi-access edge computing (MEC) provides low-latency, high-speed, and high-capacity network services for the VANETs. In this paper, we propose a novel framework for blockchain-based, hierarchical multi-access edge computing for the future VANET ecosystem (BMEC-FV). In the underlying VANET environment, we propose a trust model to ensure the security of the communication link between vehicles. Multiple MEC servers calculate the trust between vehicles through computing offloading. Meanwhile, the blockchain system plays an important role to manage the entire BMEC-FV architecture. We aim to optimize the throughput and the quality of services (QoS) for MEC users in the lower layer of the system architecture. In this framework, the main challenge is how to effectively reach consensus among blockchain nodes while ensuring the performance of MEC systems and blockchains. The blocksize of blockchain nodes, the number of consensus nodes, reliable features of each vehicle, and the number of producing blocks for each block producer are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Since it is difficult for this to be solved by traditional methods, we propose a novel deep compressed neural network scheme. Simulation results illustrate the superiority of the BMEC-FV ecosystem.

关键词:

Markov decision process Task analysis Reliability Blockchains Security mobile edge computing Markov processes blockchain Computational modeling Vehicular ad hoc networks

作者机构:

  • [ 1 ] [Zhang, Dajun]Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
  • [ 2 ] [Yu, F. Richard]Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
  • [ 3 ] [Yang, Ruizhe]Beijing Univ Technol, Beijing Lab Adv Informat Networks, Beijing 100081, Peoples R China

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来源 :

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

ISSN: 1524-9050

年份: 2021

期: 8

卷: 23

页码: 12161-12175

8 . 5 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:87

JCR分区:1

被引次数:

WoS核心集被引频次: 26

SCOPUS被引频次: 30

ESI高被引论文在榜: 0 展开所有

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