Few-Shot Transfer Learning-Based Fault Classification in Wireless Sensor Networks

This paper introduces a few-shot transfer learning approach to fault classification in wireless sensor networks (WSNs) with a minimal number of fault samples. WSNs are susceptible to various faults, such as drift, stuck, bias, and spike faults, erratic behavior, and data loss, which can compromise system reliability. Conventional deep learning fault diagnosis methods have achieved promising results, however, the majority of these approaches require a substantial amount of labeled training data, which is not available in real-world scenarios. To address this, we propose a novel method that combines convolutional neural networks (ResNet-18, VGG-16, and MobileNetV2 backbone architectures) with prototypical networks for fault diagnosis in WSNs with only a few fault samples. By transforming time-series data into Gramian Angular Field images, our approach leverages pre-trained deep learning models to extract feature-rich embeddings. These embeddings are then classified using a prototypical network, which enhances the system’s ability to diagnose faults even with a limited amount of labeled data. The proposed model is lightweight and deployable on Internet of Things (IoT) devices, ensuring efficient fault classification with minimal computational resources. Experimental results demonstrate the model’s high accuracy and robustness across various fault types, highlighting its potential for scalable and adaptive IoT applications.