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一种新的无线传感器网络中异常节点检测定位算法

蒋俊正 杨杰 欧阳缮

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. doi: 10.11999/JEIT171207
引用本文: 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. doi: 10.11999/JEIT171207
Junzheng JIANG, Jie YANG, Shan OUYANG. Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2358-2364. doi: 10.11999/JEIT171207
Citation: Junzheng JIANG, Jie YANG, Shan OUYANG. Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2358-2364. doi: 10.11999/JEIT171207

一种新的无线传感器网络中异常节点检测定位算法

doi: 10.11999/JEIT171207
基金项目: 国家自然科学基金(61761011, 61371186),广西自然科学基金(2017GXNSFAA198173),桂林电子科技大学研究生教育创新计划(2018YJCX34)
详细信息
    作者简介:

    蒋俊正:男,1983 年生,教授,博士生导师,研究方向为图信号处理理论与算法、图滤波器组设计

    杨杰:男,1991 年生,硕士生,研究方向为图信号处理理论及应用

    欧阳缮:男,1960 年生,教授,博士生导师,研究方向为自适应信号处理、通信信号处理

    通讯作者:

    蒋俊正  jzjiang@guet.edu.cn

  • 中图分类号: TP393

Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks

Funds: The National Natural Science Foundation of China (61761011, 61371186), The Natural Science Foundation of Guangxi (2017GXNSFAA198173), The Innovation Project of GUET Graduate Education (2018YJCX34)
  • 摘要: 无线传感器网络中异常节点检测是确保网络数据准确性和可靠性的关键步骤。基于图信号处理理论,该文提出了一种新的无线传感器网络异常节点检测定位算法。新算法首先对网络建立图信号模型,然后基于节点域-图频域联合分析的方法,实现异常节点的检测和定位。具体而言,第1步是利用高通图滤波器提取网络信号的高频分量。第2步首先将网络划分为多个子图,然后筛选出子图输出信号的特定频率分量。第3步对筛选出的子图信号进行阈值判断从而定位疑似异常的子图中心节点。最后通过比较各子图的节点集合和疑似异常节点集合,检测并定位出网络中的异常节点。实验仿真表明,与已有的无线传感器网络中异常检测方法相比,新算法不仅有着较高的异常检测概率,而且异常节点的定位率也较高。
  • 图  1  异常检测流程图

    图  2  预处理前后信号在节点域-图频域的对比图

    图  3  环状图模型

    图  4  美国主要城市2003年某日平均气温网络图

    图  5  海平面部分测量站点某时刻的温度传感器网络图

    表  1  环状图模型的匹配筛选表

    ${V_i}$( ${V_i}$的中心节点为 ${v_i}$) 匹配筛选 筛选结果
    ${V_1} = \{ {v_1},{v_2},{v_3}\} $ ${V_1} \not\subset {V_{\rm A}}$ ${v_1}$不是异常节点
    ${V_2} = \{ {v_1},{v_2},{v_4}\} $ ${V_2} \subset {V_{\rm A}}$ ${v_2}$是异常节点
    ${V_3} = \{ {v_1},{v_3},{v_5}\} $ ${V_3} \not\subset {V_{\rm A}}$ ${v_3}$不是异常节点
    ${V_4} = \{ {v_2},{v_4},{v_6}\} $ ${V_4} \not\subset {V_{\rm A}}$ ${v_4}$不是异常节点
    ${V_5} = \{ {v_3},{v_5},{v_6}\} $ ${V_5} \not\subset {V_{\rm A}$ ${v_5}$不是异常节点
    ${V_6} = \{ {v_4},{v_5},{v_6}\} $ ${V_6} \not\subset {V_{\rm A}}$ ${v_6}$不是异常节点
    下载: 导出CSV

    表  2  美国主要城市温度网络中单个节点信号值异常增大情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤5)
    2 98.5 94.2 64.2
    3 95.4 89.7 69.3
    4 90.6 84.5 69.0
    下载: 导出CSV

    表  3  美国主要城市温度网络中单个节点信号值异常置零情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤5)
    2 99.7 98.3 67.0
    3 99.2 97.7 76.4
    4 98.4 96.9 80.4
    下载: 导出CSV

    表  4  美国主要城市温度网络中5个节点信号值异常增大情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤10)
    2 100.0 72.5 26.1
    3 100.0 58.3 24.6
    4 99.9 46.5 20.6
    下载: 导出CSV

    表  5  美国主要城市温度网络中5个节点信号值异常置零情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤10)
    2 100.0 92.5 32.8
    3 99.9 89.9 39.9
    4 99.9 86.9 42.6
    下载: 导出CSV

    表  6  海平面部分温度站点网络单节点异常增大情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤5)
    2 99.9 99.8 78.5
    3 99.6 99.4 85.5
    4 99.0 98.8 88.6
    下载: 导出CSV

    表  7  海平面部分温度站点网络单节点异常置零情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤5)
    2 99.8 99.7 81.9
    3 99.7 99.5 87.8
    4 99.5 99.3 90.6
    下载: 导出CSV

    表  8  海平面部分温度站点网络5个节点异常增大情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤10)
    2 100.0 98.7 36.7
    3 100.0 96.7 41.4
    4 99.9 92.9 42.6
    下载: 导出CSV

    表  9  海平面部分温度站点网络5个节点异常置零情况的检测指标(%)

    $\tau $ DR OPR OPR (OS≤10)
    2 100.0 99.6 36.7
    3 100.0 99.5 41.8
    4 100.0 99.3 44.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-12-21
  • 修回日期:  2018-05-18
  • 网络出版日期:  2018-07-30
  • 刊出日期:  2018-10-01

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