The rapid expansion of the Ubiquitous Electric Internet of Things (UEIoT) has introduced a vast array of heterogeneous devices into smart grids, rendering traditional identification methods inadequate. The continuous emergence of new terminal models and frequent firmware updates create a dynamic environment where training data cannot realistically cover all evolving device types. To bridge this gap, we propose HALO (Hierarchical Attribute-guided Learning with Offset Calibration), a generalized zero-shot learning (GZSL) framework specifically designed for IoT device identification. First, a lightweight Transformer-based architecture, NetFormer, is utilized to extract discriminative features by capturing fine-grained temporal behaviors with minimal computational overhead. Second, a Weighted Conditional Variational Autoencoder (W-CVAE) is developed to synthesize high-quality pseudo-samples for unseen classes. To ensure semantic fidelity, the W-CVAE incorporates multi-scale Maximum Mean Discrepancy (MMD) to prevent mode collapse and employs attribute-feature contrastive learning to align semantic and feature spaces. Finally, a hybrid prototype construction strategy and an adaptive bias calibration mechanism are introduced to dynamically adjust decision boundaries, effectively mitigating the seen-class bias inherent in GZSL. Experimental results demonstrate that HALO significantly outperforms existing baseline methods across multiple evaluation metrics, validating the effectiveness and superiority of the proposed framework.
Wang et al. (Wed,) studied this question.
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