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Author:

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

Indexed by:

Scopus SCIE CSCD

Abstract:

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.

Keyword:

billing data interaction mobile edge computing electric vehicles blockchain reinforcement learning

Author Community:

  • [ 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

Reprint Author's Address:

  • [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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Source :

CHINA COMMUNICATIONS

ISSN: 1673-5447

Year: 2021

Issue: 8

Volume: 18

Page: 279-296

4 . 1 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:87

JCR Journal Grade:2

Cited Count:

WoS CC Cited Count: 11

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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