Detection and Localization of Outlier Nodes in Wireless Sensor Networks via Jointing Temporal and Spatial Residuals

JIN Ming; DING Rong

doi:10.12263/DZXB.20220910

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Detection and Localization of Outlier Nodes in Wireless Sensor Networks via Jointing Temporal and Spatial Residuals

PAPERS | 更新时间：2025-12-08

- Detection and Localization of Outlier Nodes in Wireless Sensor Networks via Jointing Temporal and Spatial Residuals
- ACTA ELECTRONICA SINICA Vol. 51, Issue 5, Pages: 1172-1178(2023)
- 作者机构：
  
  宁波大学信息科学与工程学院，浙江宁波 315200
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20220910
  CLC： TP393
- Received：01 August 2022，
  
  Revised：2022-10-18，
  
  Published：25 May 2023
- 稿件说明：
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金明,丁蓉.一种联合时域和空域残差的网络异常检测与节点定位方法[J].电子学报,2023,51(05):1172-1178.

JIN Ming,DING Rong.Detection and Localization of Outlier Nodes in Wireless Sensor Networks via Jointing Temporal and Spatial Residuals[J].ACTA ELECTRONICA SINICA,2023,51(05):1172-1178.
金明,丁蓉.一种联合时域和空域残差的网络异常检测与节点定位方法[J].电子学报,2023,51(05):1172-1178. DOI： 10.12263/DZXB.20220910.

JIN Ming,DING Rong.Detection and Localization of Outlier Nodes in Wireless Sensor Networks via Jointing Temporal and Spatial Residuals[J].ACTA ELECTRONICA SINICA,2023,51(05):1172-1178. DOI： 10.12263/DZXB.20220910.

摘要

无线传感器网络中，有效检测网络异常并定位异常节点是确保数据可靠性的前提.传统的基于图信号处理的网络异常检测和异常节点定位方法无法兼顾检测性能和定位性能.为克服此缺点，提出了一种联合图信号时域和空域残差的网络异常检测与节点定位方法.首先，建立一个基于历史数据相关性和节点距离的图信号模型.接着，联合图信号高频分量的时域残差和空域残差实现网络异常检测.然后，利用图信号时域残差把传感器节点分成两组.在分组过程中，通过最大化有序残差分组间的均值差将异常节点划分到同一组.最后，判定具有较大残差值分组的传感器节点为异常节点.基于全球海平面压力和温度数据的仿真结果表明了所提方法的有效性.针对异常节点海平面压力误差为4 kPa、温度误差为5 ℃和3 ℃的三种情况，与双通道图滤波方法相比，所提方法的检测概率提高了至少20%，正确定位率提高了至少15%.

Abstract

In wireless sensor networks (WSNs)

detecting the occurrence of abnormal behaviors and localizing the outlier nodes effectively are the premise for ensuring the reliability of collected data. Traditional detection and localization methods based on graph signal processing cannot achieve high performance in detection and localization simultaneously. To overcome this drawback

this work proposed a detection and localization method which jointly taken advantage of both temporal and spatial residuals of graph signals. Firstly

a graph model based on the correlations of historical data and the distances among nodes was established

and temporal and spatial residuals of high-frequency graph components were employed to detect network anomalies. Then

sensor nodes were divided into two groups using temporal residuals of graph signals

and the nodes in the group with larger temporal residuals were identified as outlier nodes. Numerical simulations based on the data sets of sea level pressure and surface temperature are provided to demonstrate the superior performance of the proposed method. Compared with the two-channel graph filtering method

the proposed method improves the performance by at least 20% in detection probability and 15% in outlier positioning rate

for the cases with an abnormal error of sea level pressure of 4 kPa and abnormal errors of temperature of 5 ℃ and 3 ℃.

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Keywords

references

LIANG W , MA C , ZHENG M , et al . Relay node placement in wireless sensor networks: From theory to practice [J]. IEEE Transactions on Mobile Computing , 2021 , 20 ( 4 ): 1602 - 1613 .

SHENG B , LI Q , MAO W , et al . Outlier detection in sensor networks [C]// Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing . New York : ACM , 2007 : 219 - 228 .

PALPANAS T , PAPADOPOULOS D , KALOGERAKI V , et al . Distributed deviation detection in sensor networks [J]. ACM Sigmod Record , 2003 , 32 ( 4 ): 77 - 82 .

LIU H , LI J , WU Y , et al . Clustering with outlier removal [J]. IEEE Transactions on Knowledge and Data Engineering , 2021 , 33 ( 6 ): 2369 - 2379 .

YANG Z , MERATNIA N , HAVINGA P . Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine [J]. Ad Hoc Networks , 2013 , 11 ( 3 ): 1062 - 1074 .

YU S , LI X , ZHAO L , et al . Hyperspectral anomaly detection based on low-rank representation using local outlier factor [J]. IEEE Geoscience and Remote Sensing Letters , 2020 , 18 ( 7 ): 1279 - 1283 .

RAMAKRISHNA R , SCGLIONE A . Grid-graph signal processing(Grid-GSP): A graph signal processing framework for the power grid [J]. IEEE Transactions on Signal Processing , 2021 , 69 : 2725 - 2739 .

SANDRYHAILA A , MOURA J M F . Discrete signal processing on graphs: frequency analysis [J]. IEEE Transactions on Signal Processing , 2014 , 62 ( 12 ): 3042 - 3054 .

蒋俊正 , 杨杰 , 欧阳缮 . 一种新的无线传感器网络中异常节点检测定位算法 [J]. 电子与信息学报 , 2018 , 40 ( 10 ): 2358 - 2364 .

JIANG J , YANG J , OUYANG S . A new detection and location algorithm for abnormal nodes in wireless sensor networks [J]. Journal of Electronics and Information , 2018 , 40 ( 10 ): 2358 - 2364 . (in Chinese)

MA M , ZHOU F , JIANG J , et al . Malfunction detection and localization algorithm for wireless sensor network [J]. Circuits Systems and Signal Processing , 2021 , 4 : 501 - 509 .

XIAO Z , FANG H , WANG X . Distributed nonlinear polynomial graph filter and its output graph spectrum: filter analysis and design [J]. IEEE Transactions on Signal Processing , 2021 , 69 : 1725 - 1739 .

YANG G , YANG L , YANG Z , et al . Efficient node selection strategy for sampling bandlimited signals on graphs [J]. IEEE Transactions on Signal Processing , 2021 , 69 : 5815 - 5829 .

DOMINGOS J , MOURA J M F . Graph Fourier transform: A stable approximation [J]. IEEE Transactions on Signal Processing , 2020 , 68 : 4422 - 4437 .

JIANG J , CHENG C , SUN Q . Nonsubsampled graph filter banks: Theory and distributed algorithms [J]. IEEE Transactions Signal Processing , 2017 , 67 ( 15 ): 3938 - 3953 .

JIANG J , FENG H , TAY D B , et al . Theory and design of joint time-vertex nonsubsampled filter banks [J]. IEEE Transactions on Signal Processing , 2021 , 69 ( 99 ): 1968 - 1982 .

CHEN J , GUPTA A K . Parametric Statistical Change Point Analysis with Applications to Genetics, Medicine, and Finance [M]. 2nd ed . New York : Springer , 2011 .

KISTLER R , KALNAY E , COLLINS W , et al . Sea level pressure, 1948-2010 [DS/OL]. [ 2020-12-1 ]. http://research. jisao.washington.edu/data_sets/reanalysis/ http://research.jisao.washington.edu/data_sets/reanalysis/ .

NOAA Physical Sciences Laboratory . Sea surface temperature(SST) [DS/OL]. [ 2020-12-1 ]. http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html .

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