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Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
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Fault Detection in Wireless Sensor Networks through the Random Forest Classifier.

Zainib Noshad1, Nadeem Javaid2, Tanzila Saba3

  • 1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan. zainabnoshad@yahoo.com.

Sensors (Basel, Switzerland)
|April 4, 2019
PubMed
Summary
This summary is machine-generated.

This study compares machine learning classifiers for fault detection in Wireless Sensor Networks (WSNs). The Random Forest (RF) algorithm demonstrated superior performance in identifying sensor faults, enhancing network reliability.

Keywords:
WSNsconvolutional neural networkfault detectionmachine learningrandom forestsupport vector machine

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Security

Background:

  • Wireless Sensor Networks (WSNs) face significant fault vulnerabilities due to unpredictable environments, leading to hardware, software, and communication failures.
  • Limited sensor resources and diverse deployment scenarios complicate fault detection in WSNs, necessitating robust methodologies.
  • Sensor-level fault classification is crucial for maintaining the integrity and performance of WSNs.

Purpose of the Study:

  • To conduct a comparative analysis of various machine learning classifiers for detecting sensor-level faults in Wireless Sensor Networks.
  • To evaluate the effectiveness of Support Vector Machine (SVM), Convolutional Neural Network (CNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Probabilistic Neural Network (PNN) for fault classification.
  • To identify the most accurate and reliable algorithm for fault detection in WSNs.

Main Methods:

  • Utilized six distinct machine learning classifiers: SVM, CNN, SGD, MLP, RF, and PNN.
  • Focused on classifying sensor-level faults including gain, offset, spike, data loss, out of bounds, and stuck-at faults.
  • Induced spike and data loss faults into real-world datasets for empirical evaluation.

Main Results:

  • Compared classifier performance using metrics such as Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score.
  • Simulations indicated that the Random Forest (RF) algorithm achieved a superior fault detection rate compared to other evaluated classifiers.
  • RF demonstrated higher accuracy in identifying induced faults like spike and data loss.

Conclusions:

  • The Random Forest (RF) algorithm is highly effective for sensor-level fault detection in Wireless Sensor Networks.
  • Comparative analysis highlights RF's advantage over other tested machine learning models in terms of fault detection accuracy.
  • The findings suggest RF as a promising solution for enhancing the reliability and robustness of WSNs against common sensor failures.