Time-Sensitive Data Processing Strategy for Enhancing the Performance of BFT Consensus Mechanism in IoT Edge Computing Environment
Cheng Qian, Wenzhong Tang, Yanyang Wang · 2023
Applying the BFT consensus mechanism in the IoT edge computing environment effectively solves the problem of data consistency and trustworthiness. However, the existing BFT consensus mechanism needs to pay attention to the impact of the data processing flow on consensus performance, and the repetitive data processing operations lead to the waste of computing resources of consensus nodes. Therefore, in this paper, we analyze the data processing flow of the BFT consensus mechanism, propose a time-sensitive data processing strategy from the idea of reducing the number of consensus nodes involved in data processing, and select data processing nodes by taking data processing time as the optimization target to improve the resource utilization of consensus nodes while ensuring consensus reaching. Through simulation experiments, our proposed time-sensitive data processing strategy achieves the best latency and throughput performance in a simulated IoT edge computing environment, demonstrating that the time-sensitive data processing strategy can effectively improve the performance of the BFT consensus mechanism.