A New Fault Method Detection for Wireless Sensor Networks using “Autoencoder and LS-SVM”

This study focuses on the issue of fault detection in WSNs while not disturbing the flow of data; and it presents a comprehensive, and new approach to dealing with the problem. The first steps in the context of the developed methodology for application to the data of stock exchanges include scaling of samples by the method of min-max, transformation of windows of samples as part of data preparation, as well as preliminary data cleaning and accurate division of data into sections. And these steps are important for dataset preparation for further analysis. The proposed method relies on the integration of Autoencoders put alongside Least Squares Support Vector Machines (LSSVM). An Autoencoder network was developed and the size of the hidden nodes was later adjusted to identify internal parameters in the dataset. It was helpful for the subsequent reconstructions of the data scene and allowed to obtain high-level features required for fault detection. With the help of these extracted features, LSSVM model was developed towards classifying no+rmal and anomalous condition in WSNs, The training outcome exhibited high effectiveness where anticipated indexes of training data set were 99.77% and for the test data set were 99%. The above outcomes support the feasibility and accuracy of the applied approach in fault recognition. The thesis greatly helps in the progression of the field by providing a methodical way of addressing the important problem of fault detection in WSNs and providing experimental evidence and analysis for the stated problem.