Machine Learning in Action: An Analysis of its Application for Fault Detection in Wireless Sensor Networks

Abstract

In a wireless sensor network (WSN), the presence of faulty nodes can cause serious problems such as data loss, reduced network life, and reduced accuracy of collected data. Therefore, the detection of failed nodes is an important task in the design and deployment of WSNs. The article discusses in detail the methodology for detecting faulty nodes in WSN and the classification of faults in WSN, and also presents a taxonomy of different types of failures in WSN. The mathematical model of WSN failure is considered. A methodology for detecting faulty nodes is shown, which includes data collection, feature extraction, training of machine learning models, and performance evaluation using appropriate metrics. Machine learning such as convolutional neural network (CNN), probabilistic neural network (PNN), multilayer perceptron (MLP), decision trees (DT), support vector machine (SVM), random forest (RF), Bayesian Belief Network (BBN), Gradient Boosting (GB) and Extreme Gradient Boosting (XGBoost). Further research is needed to improve the performance of these methods and explore the use of other algorithms to detect faulty nodes in a WSN.