Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression

Cheng, Yong; Liu, Qiuyue; Wang, Jun; Wan, Shaohua; Umer, Tariq

doi:10.1155/2018/4349795

Cited by 31 publications

(18 citation statements)

References 42 publications

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“…Likewise, in [6], the authors proposed a mechanism for fault detection based on Support Vector Regression (SVR) and neighbor coordination. They tackled a similar issue of low node densities and high failure ratios.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection in Wireless Sensor Networks through the Random Forest Classifier

Noshad

Javaid

Saba

et al. 2019

Sensors

113

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Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in unpredictable and hazardous environments. This makes WSN prone to failures such as software, hardware, and communication failures. Due to the sensor’s limited resources and diverse deployment fields, fault detection in WSNs has become a daunting task. To solve this problem, Support Vector Machine (SVM), Convolutional Neural Network (CNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Probabilistic Neural Network (PNN) classifiers are used for classification of gain, offset, spike, data loss, out of bounds, and stuck-at faults at the sensor level. Out of six faults, two of them are induced in the datasets, i.e., spike and data loss faults. The results are compared on the basis of their Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score. In this paper, a comparative analysis is performed among the classifiers mentioned previously on real-world datasets. Simulations show that the RF algorithm secures a better fault detection rate than the rest of the classifiers.

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Section: Related Workmentioning

confidence: 99%

Fault Detection in Wireless Sensor Networks through the Random Forest Classifier

Noshad

Javaid

Saba

et al. 2019

Sensors

113

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“…The subject of automatic fault and anomalies detection is particularly developed in the literature about wireless SN, but only little for noise monitoring. The description of these methods is outside the scope of this study; the reader may refer to recent references about this subject [94][95][96][97].…”

Section: Detecting Network Defaultsmentioning

confidence: 99%

Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review

Picaut

Can

Fortin

et al. 2020

Sensors

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Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

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“…It can be further extended for efficient routing/transmission. Y. Cheng et al [30] developed a fault detection scheme which utilizes super vector regression to predict the faults. Multiple nodes produce the statistics which is further used to build a prediction model and an individual node is verified on the behalf of reliable nodes only.…”

Section: Node Failure Fault Tolerance and Recovery Solutions Fomentioning

confidence: 99%

Node Failure Handling Through Localized Relocation over Wireless Sensor Network

Kaur,

Bindal

2019

IJITEE

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Communication over WSN under environmental hazards is a major issue. These constraints may have an impact over the behavior of the sensors/routing protocols and resource consumption; thus, may lead to the node failure condition i.e. software/hardware failure, security threats, excessive energy consumption, etc. It is necessary to analyze the impact of failure over network performance. In this paper, a node failure management solution is proposed, and its performance is analyzed using different protocols i.e. LEACH, AODV, and DSDV.

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Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression

Cited by 31 publications

References 42 publications

Fault Detection in Wireless Sensor Networks through the Random Forest Classifier

Fault Detection in Wireless Sensor Networks through the Random Forest Classifier

Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review

Node Failure Handling Through Localized Relocation over Wireless Sensor Network

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