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Fault Diagnosis based on Extremely Randomized Trees in Wireless Sensor Networks

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Alternative Title
Fault Diagnosis based on Extremely Randomized Trees in Wireless Sensor Networks
Abstract
Wireless Sensor Network (WSN) being highly diversified cyber?physical system makes it vulnerable to numerous failures, which can cause devastation towards safety, economy, and systems’ reliability. Precise detection and diagnosis of failures or faults in WSN is a challenging issue due to the diversity of deployment and the limitations in the sensors’ resources. In this paper, supervised machine learning-based technique is considered to scrutinize the behavior of sensors through their data for the detection and diagnosis of faults. Most of the faults that commonly occur in WSN are considered: hardover, drift, spike, erratic, data-loss, stuck, and random fault. A trusted dataset published online by the researchers at the University of North Carolina composed of temperature and humidity sensor measurements of multi-hop scenario was acquired and the aforementioned faults were simulated in non-faulty (normal) data. Events from fault occurrences were generated to replicate realistic scenarios of WSN. To detect and diagnose the faults in timely manner, we adopt an ensemble learning-based lightweight technique called Extremely Randomized Trees or Extra-Trees. The proposed Extra-Trees-based detection scheme has the ability of robustness towards signal noise and strong reduction of bias and variance error. The performances of the proposed scheme were compared with those of the state-of-the-art machine learning algorithms such as support vector machine, random forest, neural network, and decision tree. Performance evaluation shows the efficiency of the proposed scheme in terms of accuracy, precision, and F1-score. In addition, the proposed scheme has low training time compared to state-of-the-art approaches.
Keywords: Machine learning; Extremely Randomized Trees; Fault diagnosis; Classification; Wireless Sensor Networks
Author(s)
구인수Sana Ullah JanUmer SaeedYoung-Doo Lee
Issued Date
2021
Type
Article
Keyword
Machine learningExtremely Randomized TreesFault diagnosisClassificationWireless Sensor Networks
DOI
10.1016/j.ress.2020.107284
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9036
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_gale_infotracacademiconefile_A648450217&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Fault%20Diagnosis%20based%20on%20Extremely%20Randomized%20Trees%20in%20Wireless%20Sensor%20Networks&offset=0&pcAvailability=true
Publisher
RELIABILITY ENGINEERING & SYSTEM SAFETY
Location
영국
Language
영어
ISSN
0951-8320
Citation Volume
205
Citation Number
1
Citation Start Page
107284
Citation End Page
107284
Appears in Collections:
Engineering > IT Convergence
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