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作者:

Tian, Linlin (Tian, Linlin.) | Li, Meng (Li, Meng.) | Si, Pengbo (Si, Pengbo.) | Yang, Ruizhe (Yang, Ruizhe.) | Sun, Yang (Sun, Yang.) | Wang, Zhuwei (Wang, Zhuwei.)

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摘要:

Mobile edge computing technology has emerged as a novel computing paradigm that makes use of resources close to the devices of the smart rail system. Nevertheless, it is difficult to support data offloading to the stations directly from different trains due to the limited coverage of the stations equipped with MEC servers. Therefore, multi-hop ad hoc network is considered and introduced in this case. In this paper, an improved architecture is proposed for the MEC-based smart rail system by blockchain and multi-hop data communication. The requesting trains can offload the tasks to MEC servers by multi-hop transmission between trains, even when requesting trains are not covered by servers. Furthermore, we utilize the blockchain technology for the authenticity and anti-falsification of information during multi-hop transmission. Then, the offloading routing path and offloading strategy are co-optimized to minimize both delay and cost of the system. The proposed majorization problem is formulated as a Markov decision process (MDP) and solved by deep reinforcement learning (DRL). In comparison to other existing schemes, simulation results demonstrate that the proposed scheme can greatly improve system performance. © 2023 Linlin Tian et al.

关键词:

Mobile edge computing Blockchain Markov processes Ad hoc networks Reinforcement learning Deep learning

作者机构:

  • [ 1 ] [Tian, Linlin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Li, Meng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Li, Meng]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 4 ] [Si, Pengbo]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Si, Pengbo]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 6 ] [Yang, Ruizhe]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 7 ] [Yang, Ruizhe]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 8 ] [Sun, Yang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Wang, Zhuwei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

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来源 :

Mathematical Problems in Engineering

ISSN: 1024-123X

年份: 2023

卷: 2023

ESI学科: ENGINEERING;

ESI高被引阀值:19

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WoS核心集被引频次:

SCOPUS被引频次: 4

ESI高被引论文在榜: 0 展开所有

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