[1]朱海龙,耿文强,韩劲松,等.利用置信规则库构建WSN节点故障检测模型[J].智能系统学报,2021,16(3):511-517.[doi:10.11992/tis.202009006]
 ZHU Hailong,GENG Wenqiang,HAN Jinsong,et al.Constructing a WSN node fault detection model using the belief rule base[J].CAAI Transactions on Intelligent Systems,2021,16(3):511-517.[doi:10.11992/tis.202009006]
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利用置信规则库构建WSN节点故障检测模型(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年3期
页码:
511-517
栏目:
学术论文—知识工程
出版日期:
2021-05-05

文章信息/Info

Title:
Constructing a WSN node fault detection model using the belief rule base
作者:
朱海龙1 耿文强1 韩劲松2 张广玲1 冯志超3
1. 哈尔滨师范大学 计算机科学与信息工程学院,黑龙江 哈尔滨 150025;
2. 哈尔滨金融学院 计算机系,黑龙江 哈尔滨 150030;
3. 火箭军工程大学 导弹工程学院,陕西 西安 710025
Author(s):
ZHU Hailong1 GENG Wenqiang1 HAN Jinsong2 ZHANG Guangling1 FENG Zhichao3
1. School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China;
2. Department of Computer Science, Harbin Finance University, Harbin 150030, China;
3. Missile Engineeting College, Rocket Force University of Engineering, Xi’an 710025, China
关键词:
无线传感器网络故障检测数据不确定性专家知识模糊性时间相关性空间相关性置信规则库Intel Lab Data数据集
Keywords:
wireless sensor networkfault detectiondata uncertaintyexpert knowledge ambiguitytime correlationspatial correlationbelief rule baseIntel Lab Data dataset
分类号:
TP393
DOI:
10.11992/tis.202009006
摘要:
在无线传感器网络(wireless sensor network, WSN)节点故障检测领域的研究过程中,故障检测准确率会受节点数据的不确定性和专家知识模糊性的影响。针对这一问题,本文提出了一种基于置信规则库(belief rule base, BRB)的WSN节点故障检测方法。首先,根据WSN工作原理及节点工作特性描述WSN节点故障检测过程;然后,从空间和时间2个维度对节点数据提取特征,建立基于空间和时间相关性的WSN节点故障检测模型;最后,利用Intel Lab Data无线传感器数据集进行案例研究以验证模型的有效性。结果证明,本文方法能够统筹利用专家知识和节点数据实现WSN节点故障检测。
Abstract:
The WSN node fault detection accuracy is affected by uncertain factors, including the uncertainty of node data and the ambiguity of expert knowledge. This paper proposes a WSN node fault detection method based on the belief rule base. First, the WSN node fault detection process is described according to the working principle of the WSN and the working characteristics of the node. Node data are extracted from two dimensions of space and time, and then the WSN node fault detection model is established based on space and time correlation. We use the Intel lab data wireless sensor data set to conduct a case study to verify the effectiveness of the model. The experimental results indicate that the method proposed in this paper can coordinate the use of expert knowledge and node data to realize the fault detection of WSN nodes.

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备注/Memo

备注/Memo:
收稿日期:2020-09-07。
基金项目:国家自然科学基金项目(61370031,61773388);黑龙江省自然科学基金项目(F2018023)
作者简介:朱海龙,副教授,主要研究方向为模式识别、数字图像处理。参与国家自然科学基金项目1项,主持黑龙江省自然科学基金1项,黑龙江省教育厅项目1项。出版专著1部,发表学术论文20余篇;耿文强,硕士研究生,主要研究方向为故障检测、置信规则库理论;韩劲松,副教授,主要研究方向为计算机网络、数字图像处理。主持省级以上项目3项。出版专著2部,发表学术论文10余篇
通讯作者:韩劲松.E-mail:hanjinsong1970@163.com
更新日期/Last Update: 2021-06-25