一种基于分层聚合的分布式异常数据检测方案

在无线传感器网络中,由于传感器节点的带宽、功率、计算能力有限,传统的集中式方案难以区分海量数据中的异常数据.为解决此问题,提出一种基于多层分布式无线传感器网络的异常数据检测方案.在节点层级采用K-Means++算法对数据进行聚类,执行簇合并算法以减少数据传输量,在网关节点执行基于KNN的异常簇检测算法,将正常簇信息返回至底层节点进行局部检测,从而区分异常数据.在高斯数据集与IBRL数据集上的实验结果表明,该方案检测率高于98%,且能显著降低通信消耗.

A Distributed Abnormal Data Detection Scheme Based on Hierarchical Aggregation

Due to the limited bandwidth,power and computational capability of sensor nodes in the Wireless Sensor Network(WSN),the traditional scheme hardly distinguish the abnormal data accurately when faced with massive data.To address this problem,this paper proposes an abnormal data detection scheme based on hierarchical distributed WSN.The scheme clusters the data at the node level by K-Means++algorithm.Then cluster merging algorithm is used to reduce the amount of data transmission.The KNN-based abnormal cluster detection algorithm is performed on the gateway node,so as to return the normal cluster information to the underlying node for local detection,thus identifying the abnormal data.Experimental results on the Gaussian and IBRL datasets show that the detection rate of the proposed scheme is higher than 98%,and its communication consumption can be significantly reduced.