This study aim to propose a low-power, edge-deployable out-of-distribution (OOD) anomaly detection model and an anomaly-triggered time synchronization mechanism to address the challenges of anomaly detection accuracy and spatiotemporal consistency in Industrial Internet of Things (IIoT) systems, particularly for resource-constrained TinyML platforms.
The authors developed a hybrid anomaly detection model based on a one-dimensional convolutional autoencoder (1D-CAE) and one-class support vector machine. The model learns normal patterns through reconstruction error and performs anomaly detection. In addition, the authors introduce a lightweight anomaly-driven synchronization mechanism that combines regression-based clock drift correction with adaptive heartbeat signaling to ensure clock synchronization between nodes.
In the experimental environment we built, the authors’ proposed method achieved an anomaly detection accuracy of 99.8% with a timestamp alignment precision of 0.8 ms, while maintaining low power and bandwidth consumption. Compared to traditional methods, this approach reduces bandwidth consumption by 79.2% and energy consumption by 5.5%, demonstrating its efficiency and feasibility for real-world IIoT deployments.
The authors integrate OOD anomaly detection with an anomaly-triggered synchronization mechanism into a unified framework, specifically designed for resource-constrained TinyML devices. This provides an innovative solution for achieving spatiotemporal consistency in edge-based anomaly analytics. This approach holds significant theoretical and practical value for advancing efficient anomaly detection and real-time synchronization in industrial IoT applications.
