计算机科学 ›› 2023, Vol. 50 ›› Issue (6): 322-329.doi: 10.11896/jsjkx.220500171
刘炜1,2,3, 郭灵贝1,2, 夏玉洁1,2, 佘维1,2,3, 田钊1,2
LIU Wei1,2,3, GUO Lingbei1,2, XIA Yujie1,2, SHE Wei1,2,3, TIAN Zhao1,2
摘要: 在车联网中,车辆节点间需要交通信息的共享和交互,但目前还存在着节点之间难以高效同步交通数据信息以及恶意节点传播虚假信息的问题。针对上述问题,提出了一种基于信用评价模型的Raft共识算法(CE-Raft)。首先构建信用评价模型,基于孤立森林异常检测算法检测拜占庭车辆节点并将其剔除,生成诚实节点编号表;然后进行领导者选举,通过修改跟随者节点的投票过程,实现诚实节点当选领导者;最后进行日志复制,领导者节点根据诚实节点编号表发送信息同步请求,确保正确的消息在节点间达成共识。实验结果表明,CE-Raft算法能够有效排除拜占庭节点,提高了诚实节点预测准确率,具有较低的时延和较高的吞吐量,使车联网在存在恶意节点的情况下,仍然能安全高效地完成数据共享。
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