用边缘AI实现物联网设备的无线指纹识别，无需加密且高效安全。
Edge AI-based Radio Frequency Fingerprinting for IoT Networks
- 在树莓派上部署轻量级深度学习模型，从射频信号中提取设备特有指纹。
- Transformer-Encoder模型准确率超95%，AUC超90%，仅73KB大小。
- 适合资源受限的物联网设备，可实现实时边缘认证，替代复杂加密。
智能城市与关键基础设施中物联网的部署虽提升了连接性与实时数据交换能力，但也带来了显著的安全挑战。传统密码学对小型、资源受限的物联网设备而言往往开销过大。射频指纹（RFF）通过物理层独特的无线信号特征实现设备身份识别，是一种无需加密的潜在替代方案。然而，大规模部署于资源受限环境仍面临挑战。本文提出两种真正轻量级的边缘AI-RFF方案，针对边缘设备优化。采用卷积神经网络（CNN）与Transformer-Encoder模型，从IQ采样中提取复杂特征以形成设备专属指纹。模型经转换为TensorFlow Lite后，在树莓派上部署并评估，验证了边缘部署的可行性。实验表明，Transformer-Encoder优于CNN，识别准确率>0.95，ROC-AUC>0.90，模型大小仅为73KB，适合资源受限设备。
原文摘要 · Abstract (English)
The deployment of the Internet of Things (IoT) in smart cities and critical infrastructure has enhanced connectivity and real-time data exchange but introduced significant security challenges. While effective, cryptography can often be resource-intensive for small-footprint resource-constrained (i.e., IoT) devices. Radio Frequency Fingerprinting (RFF) offers a promising authentication alternative by using unique RF signal characteristics for device identification at the Physical (PHY)-layer, without resorting to cryptographic solutions. The challenge is two-fold: how to deploy such RFF in a large scale and for resource-constrained environments. Edge computing, processing data closer to its source, i.e., the wireless device, enables faster decision-making, reducing reliance on centralized cloud servers. Considering a modest edge device, we introduce two truly lightweight Edge AI-based RFF schemes tailored for resource-constrained devices. We implement two Deep Learning models, namely a Convolution Neural Network and a Transformer-Encoder, to extract complex features from the IQ samples, forming device-specific RF fingerprints. We convert the models to TensorFlow Lite and evaluate them on a Raspberry Pi, demonstrating the practicality of Edge deployment. Evaluations demonstrate the Transformer-Encoder outperforms the CNN in identifying unique transmitter features, achieving high accuracy (> 0.95) and ROC-AUC scores (> 0.90) while maintaining a compact model size of 73KB, appropriate for resource-constrained devices.
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