Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach

This research proposes a lightweight hybrid approach for anomaly detection in correlated IoT sensor data, combining PCA for fast monitoring and Autoencoders for deeper analysis. Validated on real and simulated data, the method offers high accuracy, faster response, and fewer false positives—ideal for resource-constrained environments.