PBRL-TChain: A performance-enhanced permissioned blockchain for time-critical applications based on reinforcement learning

Yiguang Zhang, Junxiong Lin, Zhihui Lu, Qiang Duan, Shih Chia Huang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Permissioned blockchains can provide high security and reliability for various Internet of Things (IoT) systems, such as smart healthcare and vehicular networks. However, the performance issues of permissioned blockchains have been a constraint for fully supporting time-critical tasks with tight requirements of low latency and high throughput. This paper proposes PBRL-TChain, a performance-enhanced permissioned blockchain for time-critical applications based on deep reinforcement learning (DRL). First, we propose a priority ordering mechanism to minimize latency and maximize reliability. Then, we design a fast retransmission mechanism to alleviate the impact of transaction conflicts on the latency performance. Finally, we propose a DRL-based dynamic adjustment method in PBRL-TChain to achieve better performance and reliability. Experiments show that our method outperforms existing methods. Compared with Fabric++ and Athena, it can reduce the latency of time-critical transactions by 10 times, achieving a level of 10 ms, significantly improving the system's performance and reliability.

Original languageEnglish (US)
Pages (from-to)301-313
Number of pages13
JournalFuture Generation Computer Systems
StatePublished - May 2024

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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