Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
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, fa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/7/1568 |
| _version_ | 1798041246801854464 |
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| author | Zainib Noshad Nadeem Javaid Tanzila Saba Zahid Wadud Muhammad Qaiser Saleem Mohammad Eid Alzahrani Osama E. Sheta |
| author_facet | Zainib Noshad Nadeem Javaid Tanzila Saba Zahid Wadud Muhammad Qaiser Saleem Mohammad Eid Alzahrani Osama E. Sheta |
| author_sort | Zainib Noshad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:18:48Z |
| format | Article |
| id | doaj.art-127af08263524d75a308ef6db9217246 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:18:48Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-127af08263524d75a308ef6db92172462022-12-22T04:00:16ZengMDPI AGSensors1424-82202019-04-01197156810.3390/s19071568s19071568Fault Detection in Wireless Sensor Networks through the Random Forest ClassifierZainib Noshad0Nadeem Javaid1Tanzila Saba2Zahid Wadud3Muhammad Qaiser Saleem4Mohammad Eid Alzahrani5Osama E. Sheta6Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, PakistanDepartment of Computer Science, COMSATS University Islamabad, Islamabad 44000, PakistanCollege of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaDepartment of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, PakistanCollege of Computer Science and Information Technology, Al Baha University, Al Baha 11074, Saudi ArabiaCollege of Computer Science and Information Technology, Al Baha University, Al Baha 11074, Saudi ArabiaCollege of Science, Zagazig University, Zagazig 44511, EgyptWireless 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.https://www.mdpi.com/1424-8220/19/7/1568WSNsfault detectionmachine learningrandom forestsupport vector machineconvolutional neural network |
| spellingShingle | Zainib Noshad Nadeem Javaid Tanzila Saba Zahid Wadud Muhammad Qaiser Saleem Mohammad Eid Alzahrani Osama E. Sheta Fault Detection in Wireless Sensor Networks through the Random Forest Classifier Sensors WSNs fault detection machine learning random forest support vector machine convolutional neural network |
| title | Fault Detection in Wireless Sensor Networks through the Random Forest Classifier |
| title_full | Fault Detection in Wireless Sensor Networks through the Random Forest Classifier |
| title_fullStr | Fault Detection in Wireless Sensor Networks through the Random Forest Classifier |
| title_full_unstemmed | Fault Detection in Wireless Sensor Networks through the Random Forest Classifier |
| title_short | Fault Detection in Wireless Sensor Networks through the Random Forest Classifier |
| title_sort | fault detection in wireless sensor networks through the random forest classifier |
| topic | WSNs fault detection machine learning random forest support vector machine convolutional neural network |
| url | https://www.mdpi.com/1424-8220/19/7/1568 |
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