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.

为了提高绿色物联网（G-IoT）中区块链的共识性能，并在实际拜占庭容错（PBFT）共识算法中改善静态网络结构和通信开销，本文提出了一种信用增强拜占庭式故障使用强化学习的公差（CRBFT）共识算法。CRBFT算法将节点分为三种类型，每种类型具有不同的职责：主节点，子节点和候选节点，并将信用属性设置为该节点。节点的信用可以通过强化学习算法进行自适应调整，该算法可以动态更改节点的状态。CRBFT算法可以自动识别恶意节点和无效节点，使它们退出共识网络。实验结果表明，CRBFT算法可以有效地提高共识网络的安全性。此外，与PBFT算法相比，在CRBFT中，共识延迟减少了约40％，流量开销减少了45％以上。这种减少有利于节省能源并减少排放。