Cell Based Raft Algorithm for Optimized Consensus Process on Blockchain in Smart Data Market

作     者：Yang, Dana Doh, Inshil Chae, Kijoon 

作者机构：Ewha Womans Univ Dept Comp Sci & Engn Seoul 03760 South Korea Ewha Womans Univ Dept Cyber Secur Seoul 03760 South Korea 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2022年第10卷

页      面：85199-85212页

核心收录：

基　　金：National Research Foundation of Korea (NRF) - Korea government (MSIP) [2020R1A2C1006497] 

主　　题：Blockchains Servers Consensus algorithm Collaborative work Internet of Things Throughput Smart devices Data science Smart service blockchain consensus algorithm raft algorithm federated learning 

摘      要：Due to the explosive increase in IoT devices and traffic, big data is developing into smart data that helps the data science experts understand human activities, through the relationship between mobility and resource application of the users in public spaces. For example, smart data markets help to predict crimes or understand the cause of COVID-19 infections. For these smart services, the users agree to the privacy policy so that the personal and sensitive information can be collected by a third party. But the conditions of the privacy policy do not specify whether the information of the users can be tracked. To ensure data transparency, many systems are applying consortium/private blockchains with raft algorithm. The raft algorithm requires nodes to check countless messages for a single transaction. Eventually, as the number of nodes increases, the overall system degradation is derived from the burden of the leader node. This paper proposes a method to process the collected transactions by dividing a certain amount of transactions into cells, without any extra protocol. The proposed scheme also uses the federated learning model with high accuracy and data privacy, in order to determine the optimized cell size in a blockchain system that should lead to consensus on multiple servers. Therefore, the proposed CBR (Cell-based Raft) consensus algorithm proposes a protocol that reduces the number of messages, without interfering with the concept of the existing raft algorithm, in order to maintain stable throughput in the smart data market where massive transactions occur.