# Edge AI-based Radio Frequency Fingerprinting for IoT Networks

arXiv: https://arxiv.org/abs/2412.10553 · HTML: https://mlsanity.com/paper/2412.10553/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting](https://mlsanity.com/paper/2112.15363/) (2021)
- [Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa](https://mlsanity.com/paper/2107.02867/) (2021)
- [Edge Intelligence for Empowering IoT-based Healthcare Systems](https://mlsanity.com/paper/2103.12144/) (2021)
- [Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices](https://mlsanity.com/paper/2111.14275/) (2021)
- [Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/2310.16406/) (2023)
- [Security in Energy Harvesting Networks: A Survey of Current Solutions and Research Challenges](https://mlsanity.com/paper/2004.10394/) (2020)

## Built on by (most cited first, 3 of 3)

- [AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons](https://mlsanity.com/paper/2505.10273/) (2025)
- [Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection](https://mlsanity.com/paper/2512.15503/) (2025)
- [HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD](https://mlsanity.com/paper/2507.12133/) (2025)

## Abstract (arXiv)

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.