Real-Time Event Recognition of Long-Distance Distributed Vibration Sensing With Knowledge Distillation and Hardware Acceleration

Fiber-optic sensing, especially distributed optical fiber vibration (DVS) sensing, is gaining importance in Internet of Things (IoT) applications, such as industrial safety monitoring and intrusion detection. Despite their wide application, existing post-processing methods that rely on deep learning models for event recognition in DVS systems face challenges with real-time processing of large sample data volumes, particularly in long-distance applications. To address this issue, we propose to use a four-layer convolutional neural network (CNN) as the student model with ResNet as the teacher model for knowledge distillation. Compared to the baseline CNN model, which achieves 83.41% accuracy on data from previously untrained environments, the distilled CNN improves accuracy significantly to 95.39%, demonstrating its superior generalizability and robustness. Additionally, we propose a novel hardware design based on field-programmable gate arrays to further accelerate model inference. This design replaces multiplication with binary shift operations and quantizes model weights, enabling high parallelism and low latency. Our implementation achieves an inference time of 0.083 ms for a spatial-temporal sample covering a 12.5 m fiber length and 0.256 s time frame. This performance enables real-time signal processing over approximately 38.55 km of fiber, about $2.14\times $ the capability of an Nvidia GTX 4090 graphics processing unit. The proposed method greatly enhances the efficiency of vibration pattern recognition, promoting the use of DVS as a smart IoT system. The data and code are available at https://github.com/HUST-IOF/Efficient-DVS.