Proactive Network Health Management in WSNs: An Integrated Machine Learning and Deep Learning Framework

Wireless Sensor Networks (WSNs) face major difficulties in sustaining network health because of their restricted resources and their need to operate in dangerous environments and their need to defend against new cyber-attacks. The paper presents a comprehensive health management framework for WSN networks which uses Principal Component Analysis (PCA) to reduce data dimensions and employs K-Nearest Neighbors and Random Forest and Support Vector Machines as machine learning classifiers and uses Long Short-Term Memory (LSTM) networks for spatial-temporal analysis and K-Means clustering for pattern discovery. The framework achieves 99.46% accuracy in detecting anomalies through its KNN method and 95.83% accuracy in predicting temporal patterns through its LSTM method and it achieves strong cluster separation with a silhouette score of 0.72 while it reduces 70 features to 2 principal components which maintain 35.1% variance for better processing speed. The system provides real-time anomaly detection and predictive degradation forecasting and energy-efficient operations which solve the main problems with existing WSN monitoring systems and it helps to extend node life by 20-30% through its early intervention capability.