Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
1
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2
College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
3
Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan
4
College of Computer Science and Information Technology, Al Baha University, Al Baha 11074, Saudi Arabia
5
College of Science, Zagazig University, Zagazig 44511, Egypt
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1568; https://doi.org/10.3390/s19071568
Received: 4 January 2019
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Revised: 20 February 2019
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Accepted: 25 February 2019
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Published: 1 April 2019
(This article belongs to the Section Sensor Networks)
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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Keywords:
WSNs; fault detection; machine learning; random forest; support vector machine; convolutional neural network
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MDPI and ACS Style
Noshad, Z.; Javaid, N.; Saba, T.; Wadud, Z.; Saleem, M.Q.; Alzahrani, M.E.; Sheta, O.E. Fault Detection in Wireless Sensor Networks through the Random Forest Classifier. Sensors 2019, 19, 1568. https://doi.org/10.3390/s19071568
AMA Style
Noshad Z, Javaid N, Saba T, Wadud Z, Saleem MQ, Alzahrani ME, Sheta OE. Fault Detection in Wireless Sensor Networks through the Random Forest Classifier. Sensors. 2019; 19(7):1568. https://doi.org/10.3390/s19071568
Chicago/Turabian StyleNoshad, Zainib, Nadeem Javaid, Tanzila Saba, Zahid Wadud, Muhammad Q. Saleem, Mohammad E. Alzahrani, and Osama E. Sheta. 2019. "Fault Detection in Wireless Sensor Networks through the Random Forest Classifier" Sensors 19, no. 7: 1568. https://doi.org/10.3390/s19071568
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