考虑温度变化，提升蓝牙设备指纹认证的稳定性
Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy
- 将设备温度信息融入模型训练，增强抗温漂能力
- 在未知温控环境下准确率显著提升，最高达92.3%
- 适合部署于移动设备或物联网场景的可靠身份验证
射频指纹识别（RFFP）通过利用发射信号中的硬件特异性失真实现设备认证，但现有方法普遍忽略温度影响。温度作为受内部与环境因素共同作用的关键变量，会显著改变设备特征并降低分类性能。本文提出一种新型温度感知的RFFP框架，将设备温度信息显式引入学习过程，以提升鲁棒性与泛化能力。我们在多设备、多环境条件下采集的真实蓝牙低功耗（BLE）数据集上进行评估。实验结果表明，该方法在未知温度和环境条件下均显著优于其他温度缓解基线，分类准确率有明显提升。
原文摘要 · Abstract (English)
Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments embedded in transmitted signals. Yet existing methods largely overlook a major drawback: RFFP sensitivity to temperature--a critical factor influenced by both internal and environmental conditions--which can significantly alter device signatures and degrade classification performance. In this paper, we propose a novel temperature-aware RFFP framework that explicitly incorporates device temperature information into the learning process to improve robustness and generalization. We evaluate the proposed method on a real-world Bluetooth Low Energy (BLE) dataset collected across multiple devices and environmental conditions. Experimental results demonstrate that temperature-aware modeling consistently outperforms other temperature mitigation baselines, achieving significant improvements in classification accuracy, particularly under unseen temperature and environmental conditions.
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