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作者:

Ye, Xinyu (Ye, Xinyu.) | Li, Meng (Li, Meng.) | Si, Pengbo (Si, Pengbo.) | Yang, Ruizhe (Yang, Ruizhe.) | Sun, Enchang (Sun, Enchang.) | Zhang, Yanhua (Zhang, Yanhua.) (学者:张延华)

收录:

SCIE CSCD

摘要:

Recently, electric vehicles (EVs) have been widely used under the call of green travel and environmental protection, and diverse requirements for charging are also increasing gradually. In order to ensure the authenticity and privacy of charging information interaction, blockchain technology is proposed and applied in charging station billing systems. However, there are some issues in blockchain itself, including lower computing efficiency of the nodes and higher energy consumption in the consensus process. To handle the above issues, in this paper, combining blockchain and mobile edge computing (MEC), we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process. By jointly optimizing the primary and replica nodes offloading decisions, block size and block interval, the transaction throughput of the blockchain system is maximized, as well as the latency and energy consumption of the system are minimized Moreover, we formulate the joint optimization problem as a Markov decision process (MDP). To tackle the dynamic and continuity of the system state, the reinforcement learning (RL) is introduced to solve the MDP problem. Finally, simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes.

关键词:

billing data interaction blockchain electric vehicles mobile edge computing reinforcement learning

作者机构:

  • [ 1 ] [Ye, Xinyu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Meng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Si, Pengbo]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Yang, Ruizhe]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Sun, Enchang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Zhang, Yanhua]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 7 ] [Li, Meng]Beijing Lab Adv Informat Networks, Beijing 100124, Peoples R China
  • [ 8 ] [Si, Pengbo]Beijing Lab Adv Informat Networks, Beijing 100124, Peoples R China
  • [ 9 ] [Yang, Ruizhe]Beijing Lab Adv Informat Networks, Beijing 100124, Peoples R China
  • [ 10 ] [Zhang, Yanhua]Beijing Lab Adv Informat Networks, Beijing 100124, Peoples R China

通讯作者信息:

  • [Li, Meng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Li, Meng]Beijing Lab Adv Informat Networks, Beijing 100124, Peoples R China

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来源 :

CHINA COMMUNICATIONS

ISSN: 1673-5447

年份: 2021

期: 8

卷: 18

页码: 279-296

4 . 1 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:11

被引次数:

WoS核心集被引频次: 9

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

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