Optimization of PBFT Algorithm Based on QoS-Aware Trust Service Evaluation

Sensors (Basel). 2022 Jun 17;22(12):4590. doi: 10.3390/s22124590.

Abstract

In service-transaction scenarios, blockchain technology is widely used as an effective tool for establishing trust between service providers and consumers. The consensus algorithm is the core technology of blockchain. However, existing consensus algorithms, such as the practical Byzantine fault tolerance (PBFT) algorithm, still suffer from high resource consumption and latency. To solve this problem, in this study, we propose an improved PBFT blockchain consensus algorithm based on quality of service (QoS)-aware trust service evaluation for secure and efficient service transactions. The proposed algorithm, called the QoS-aware trust practical Byzantine fault tolerance (QTPBFT) algorithm, efficiently achieves consensus, significantly reduces resource consumption, and enhances consensus efficiency. QTPBFT incorporates a QoS-aware trust service global evaluation mechanism that implements service reliability ranking by conducting a dynamic evaluation according to the real-time performance of the services. To reduce the traffic of the blockchain, it uses a mechanism that selects nodes with higher values to form a consensus group that votes for consensus according to the global evaluation result of the trust service. A practical protocol is also constructed for the proposed algorithm. The results of extensive simulations and comparison with other schemes verify the efficacy and efficiency of the proposed scheme.

Keywords: PBFT; QoS-aware; blockchain; consensus mechanism.

MeSH terms

  • Algorithms
  • Blockchain*
  • Reproducibility of Results
  • Trust*
  • Wireless Technology

Grants and funding

This work was funded by the National Key Research and Development Project under Grant 2018YFB1404400, the National Natural Science Foundation of China under Grant 62062030, Hainan Provincial Natural Science Foundation of China under Grant Number 620RC620, the Major Science and Technology Project of Haikou under Grant 2020-009, and Key R&D Project of Hainan province under Grant ZDYF2021SHFZ243.