Research on the efficient performance algorithm of fragmented blockchain for industrial Internet of Things applications
Yanbo Yin, Jianming Zhao, Tianyu Wang, Weiyan Tong · 2023
This article addresses the challenge of optimizing performance in a fragmentation blockchain system for industrial Internet of things (IIOT) applications. It introduces an enhanced version of the traditional Whale Optimization Algorithm (WOA) known as the Chaotic Whale Optimization Algorithm. This enhanced algorithm is designed to optimize the parameters of the fragmentation blockchain system with the goal of maximizing real-time memory utilization in the fragmentation nodes while minimizing block consensus time. The proposed method identifies optimal blockchain system parameters within diverse network environments, taking into account factors such as node computing power and inter-node transmission rates. To mitigate the issue of local optima inherent in traditional algorithms, the Chaotic Whale Optimization Algorithm transforms the optimization problem in a more intuitive manner. This algorithm strikes a fine balance between diversity and convergence. The effectiveness of this method is assessed through simulation experiments, with results indicating its superior performance compared to alternative approaches. Overall, the approach presented in this article significantly reduces consensus time within the fragmentation blockchain system and enhances real-time memory utilization in the fragmented blocks.