A novel Byzantine fault tolerance consensus for Green IoT with intelligence based on reinforcement

Abstract

To enhance the consensus performance of Blockchain in the Green Internet of Things (G-IoT) and improve the static network structure and communication overheads in the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, in this paper, we propose a Credit Reinforce Byzantine Fault Tolerance (CRBFT) consensus algorithm by using reinforcement learning. The CRBFT algorithm divides the nodes into three types, each with different responsibilities: master node, sub-nodes, and candidate nodes, and sets the credit attribute to the node. The node’s credit can be adjusted adaptively through the reinforcement learning algorithm, which can dynamically change the state of nodes. CRBFT algorithm can automatically identify malicious nodes and invalid nodes, making them exit from the consensus network. Experimental results show that the CRBFT algorithm can effectively improve the consensus network’s security. Besides, compared with the PBFT algorithm, in CRBFT, the consensus delay is reduced by about 40%, and the traffic overhead is reduced by more than 45%. This reduction is conducive to save energy and reduce emissions.