Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 783-787.DOI: 10.11772/j.issn.1001-9081.2019071224

• Network and communications • Previous Articles     Next Articles

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

LU Guangyue1,2, ZHOU Liang1,2, LYU Shaoqing1,2, SHI Cong1,2, SU Keke1,2   

  1. 1. School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an Shaanxi 710121, China;
    2. Shaanxi Key Laboratory of Information Communication Network and Security;(Xi'an University of Posts and Telecommunications), Xi'an Shaanxi 710121, China
  • Received:2019-07-15 Revised:2019-09-17 Online:2020-03-10 Published:2019-09-29
  • Supported by:
    This work is partially supported by the Scientific Research Program of Shaanxi Provincial Education Department (17JK0703).


卢光跃1,2, 周亮1,2, 吕少卿1,2, 施聪1,2, 苏可可1,2   

  1. 1. 西安邮电大学 通信与信息工程学院, 西安 710121;
    2. 陕西省信息通信网络及安全重点实验室(西安邮电大学), 西安 710121
  • 通讯作者: 周亮
  • 作者简介:卢光跃(1971-),男,河南南阳人,教授,博士,主要研究方向:信号与信息处理、频谱感知、大数据分析;周亮(1994-),男,陕西西安人,硕士研究生,主要研究方向:大数据分析、图信号处理;吕少卿(1987-),男,山西五寨人,讲师,博士,主要研究方向:社交网络、大数据分析;施聪(1994-),男,陕西西安人,硕士研究生,主要研究方向:深度学习、频谱感知;苏可可(1996-),女,陕西渭南人,硕士研究生,主要研究方向:大数据分析、图信号处理。
  • 基金资助:

Abstract: 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.

Key words: Wireless Sensor Network (WSN), graph signal processing, outlier node detection, graph low-pass filter, smoothness

摘要: 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。

关键词: 无线传感器网络, 图信号处理, 异常节点检测, 图低通滤波器, 平滑度

CLC Number: