基于图信号处理的无线传感器网络异常节点检测算法

针对无线传感器网络（WSN）中传感器自身安全性低、检测区域恶劣及资源受限造成节点采集数据异常的问题，提出一种基于图信号处理的WSN异常节点检测算法。首先，依据传感器位置特征建立K-近邻（KNN）图信号模型；然后，基于图信号在低通滤波前后的平滑度之比构建统计检验量；最后，通过统计检验量与判决门限实现异常节点存在性的判断。通过在公开的气温数据集与PM2.5数据集上的仿真验证，实验结果表明，与基于图频域异常检测算法相比，在单个节点异常情况相同条件下，所提算法检测率提升7个百分点；在多个节点异常情况相同条件下，其检测率均达到98%，并且在网络节点异常偏离值较小时仍具有较高的检测率。

Outlier node detection algorithm in wireless sensor networks based ongraph signal processing

Since the low security of sensors, poor detection area and resource limitation in Wireless Sensor Network (WSN) cause outlier data collected by nodes, an algorithm of the outlier node detection in WSN based on graph signal processing was proposed. Firstly, according to the sensor position features, a K-Nearest Neighbors (KNN) graph signal model was established. Secondly, the statistical test quantity was built based on the smoothness ratio of the graph signal before and after low-pass filtering. Finally, the judgement of the existence of outlier nodes was realized through the statistical test quantity and decision threshold. Experiments on the public temperature dataset and PM2.5 dataset demonstrate that compared with algorithm of outlier node detection based on graph frequency domain, the proposed algorithm has the detection rate increased by 7% under the condition of single outlier node and has the detection rate of 98% under the condition of multiple outlier nodes, and keep high detection rate under the condition of outlier node with small deviation value.