Event Detection in Wireless Sensor Networks Using Machine Learning and Deep Learning: A Comparative Analysis for Smart Environments

The Wireless Sensor Networks (WSNs) have a central place in the facilitation of smart environments through the provision of continuous monitoring features and real-time data collection features in various fields of use including environmental monitoring, smart cities, healthcare and industrial automation. One of the most crucial issues in the WSNs is the accurate real-time detection of events in WSNs subject to limitations of limited energy, noisy data and dynamic network conditions. The classical rule-based and threshold-based methods are not always flexible to new and dynamic trends in sensor data. In this paper, a detailed comparative study of machine learning methods in the detection of events in WSNs is described. Various models are considered such as Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN) and Deep Learning models such as Long Short-Memory (LSTM) and see how well each of them can detect regardless of computational resource utilization and energy consideration. The proposed structure combines the preprocessing of the data, the extraction of the features and model optimization to improve the detection performance in smart environments. The re