A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks
In wireless sensor networks (WSNs), there are many challenges for outlier detection, such as fault detection, fraud detection, intrusion detection, and so on. In this paper, the participation degree of instances in the hierarchical clustering process infers the relationship between instances. Howeve...
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doaj-95a6981360e940cc838b46d8f94e69502020-11-24T20:42:10ZengMDPI AGSensors1424-82202019-03-01197152210.3390/s19071522s19071522A Participation Degree-Based Fault Detection Method for Wireless Sensor NetworksWei Zhang0Gongxuan Zhang1Xiaohui Chen2Xiumin Zhou3Yueqi Liu4Junlong Zhou5Computer Science and Engineering, Nanjing University of Science and Technology, NO. 200 Xiaolingwei Road, Nanjing 210094, ChinaComputer Science and Engineering, Nanjing University of Science and Technology, NO. 200 Xiaolingwei Road, Nanjing 210094, ChinaComputer Science and Engineering, Nanjing University of Science and Technology, NO. 200 Xiaolingwei Road, Nanjing 210094, ChinaComputer Science and Engineering, Nanjing University of Science and Technology, NO. 200 Xiaolingwei Road, Nanjing 210094, ChinaComputer Science and Engineering, Nanjing University of Science and Technology, NO. 200 Xiaolingwei Road, Nanjing 210094, ChinaComputer Science and Engineering, Nanjing University of Science and Technology, NO. 200 Xiaolingwei Road, Nanjing 210094, ChinaIn wireless sensor networks (WSNs), there are many challenges for outlier detection, such as fault detection, fraud detection, intrusion detection, and so on. In this paper, the participation degree of instances in the hierarchical clustering process infers the relationship between instances. However, most of the existing algorithms ignore such information. Thus, we propose a novel fault detection technique based on the participation degree, called fault detection based on participation degree (FDP). Our algorithm has the following advantages. First, it does not need data training in labeled datasets; in fact, it uses the participation degree to measure the differences between fault points and normal points without setting distance or density parameters. Second, FDP can detect global outliers without local cluster influence. Experimental results demonstrate the performance of our approach by applying it to synthetic and real-world datasets and contrasting it with four well-known techniques: isolation forest (IF), local outlier factor (LOF), one-class support vector machine (OCS), and robust covariance (RC).https://www.mdpi.com/1424-8220/19/7/1522outlier detectionfault detectionparticipation degreehierarchical clusteringWSNs |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Zhang Gongxuan Zhang Xiaohui Chen Xiumin Zhou Yueqi Liu Junlong Zhou |
spellingShingle |
Wei Zhang Gongxuan Zhang Xiaohui Chen Xiumin Zhou Yueqi Liu Junlong Zhou A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks Sensors outlier detection fault detection participation degree hierarchical clustering WSNs |
author_facet |
Wei Zhang Gongxuan Zhang Xiaohui Chen Xiumin Zhou Yueqi Liu Junlong Zhou |
author_sort |
Wei Zhang |
title |
A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks |
title_short |
A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks |
title_full |
A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks |
title_fullStr |
A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks |
title_full_unstemmed |
A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks |
title_sort |
participation degree-based fault detection method for wireless sensor networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-03-01 |
description |
In wireless sensor networks (WSNs), there are many challenges for outlier detection, such as fault detection, fraud detection, intrusion detection, and so on. In this paper, the participation degree of instances in the hierarchical clustering process infers the relationship between instances. However, most of the existing algorithms ignore such information. Thus, we propose a novel fault detection technique based on the participation degree, called fault detection based on participation degree (FDP). Our algorithm has the following advantages. First, it does not need data training in labeled datasets; in fact, it uses the participation degree to measure the differences between fault points and normal points without setting distance or density parameters. Second, FDP can detect global outliers without local cluster influence. Experimental results demonstrate the performance of our approach by applying it to synthetic and real-world datasets and contrasting it with four well-known techniques: isolation forest (IF), local outlier factor (LOF), one-class support vector machine (OCS), and robust covariance (RC). |
topic |
outlier detection fault detection participation degree hierarchical clustering WSNs |
url |
https://www.mdpi.com/1424-8220/19/7/1522 |
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