To address the issue of node overload and even failure due to resource constraints in the Industrial Internet of Things (IIoT) edge computing environment, the author proposes a fault tolerance method based on edge intelligence called SGAT-GAN (Sparse Graph Attention-based Generative Adversarial Network). This method integrates a fault prediction mechanism and utilizes Fault Embedding Encoder (FEE) technology to achieve real-time detection, classification, and prediction of faults, thereby enhancing the system’s fault tolerance capability.
On this basis, the method combines Generative Adversarial Networks (GAN) and proactive migration to select optimal strategies for intelligent remediation of faults, providing an efficient and intelligent solution for edge computing systems in the Industrial Internet of Things. Experimental results conducted in a Raspberry Pi-based edge environment indicate that this method outperforms four advanced methods: Proactive Coordinated Fault Tolerance (PCFT), Clustering-based Multi-objective Dynamic Load Balancing Technology (CMODLB), Self-supervised Deep Agent Model-based Fault Tolerance Method (DeepFT), and Pre-migration Prediction Network (PreGAN) in terms of average fault detection accuracy and average classification accuracy.
Specifically, compared to the best-performing PreGAN method, the author’s approach improves these two metrics by 1.96 percentage points and 1.62 percentage points, respectively, validating the effectiveness and superiority of this method in enhancing the reliability of industrial IoT edge computing systems.
References:[1] Niu Yuqing, Zhang Zhiyong, Zhang Zhongya, et al. Fault Tolerance Method for Industrial IoT Edge Computing Based on Sparse Graph Attention [J/OL]. Computer Applications.
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