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

Abstract

The growing adoption of IoT systems in industries like transportation, banking, healthcare, and smart energy has increased reliance on sensor networks. However, anomalies in sensor readings can undermine system reliability, making real-time anomaly detection essential. While a large body of research addresses anomaly detection in IoT networks, few studies focus on correlated sensor data streams, such as temperature and pressure within a shared space, especially in resource-constrained environments. To address this, we propose a novel hybrid machine learning approach combining Principal Component Analysis (PCA) and Autoencoders. In this method, PCA continuously monitors sensor data and triggers the Autoencoder when significant variations are detected. This hybrid approach, validated with real-world and simulated data, shows faster response times and fewer false positives. The F1 score of the hybrid method is comparable to Autoencoder, with much faster response time which is driven by PCA.