Anomalies Detection in Wireless Sensor Networks with Exploring Various Machine Learning Techniques: Review

Abstract

Wireless Sensor Networks (WSNs) form the backbone of numerous critical applications, ranging from environmental monitoring to defense surveillance, necessitating highly reliable anomaly detection systems to ensure operational integrity and security. Traditional anomaly detection methods in WSNs often grapple with the high dimensionality of sensor data, dynamic environmental conditions, and resource constraints, leading to suboptimal performance. This research paper introduces a novel framework that leverages advanced machine learning techniques, focusing on utilizing deep learning techniques that can markedly improve the precision in identifying irregularities within Wireless Sensor Networks (WSNs). By employing a comprehensive methodology that encompasses data preprocessing, feature engineering, and the deployment of sophisticated Models based on deep learning, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this study demonstrates a marked improvement in detecting abnormal events within sensor data streams. The proposed models are evaluated against traditional machine learning benchmarks on a collection of performance indicators such as correctness, exactness, sensitivity, and the F1 metric., showcasing their superior ability to generalize and detect anomalies under varied conditions. This research not only addresses the inherent challenges faced by WSNs but also sets a precedent for the integration of cutting-edge machine learning algorithms in enhancing network reliability and security. The outcomes of this research hold considerable importance for advancing anomaly detection within Wireless Sensor Networks (WSNs), setting the stage for developing more robust and smart systems.