Energy-Aware RF Fingerprinting for Device Identification in Ultra-Low-Power IoT Systems

Abstract

The security of ultra-low-power Internet of Things (IoT) systems is critical yet challenging due to significant energy constraints. These networks are vulnerable to impersonation and data poisoning attacks, where malicious entities can mimic legitimate devices to gain access or corrupt system integrity. While traditional cryptographic solutions are often too energy-intensive for these environments, radio frequency (RF) fingerprinting offers a promising physical layer security alternative by using intrinsic hardware imperfections to uniquely identify devices. However, existing RF fingerprinting methods often overlook the severe energy budgets of battery-powered IoT devices. To address this challenge, this paper introduces two complementary deep learning models for device identification in long range wide area network systems. The first, RFNet, is a full-capacity convolutional neural network that achieves 97.48% identification accuracy. The second, TinyRFNet, is an ultra-lightweight model designed for resource-constrained hardware, maintaining 93.19% accuracy with over 34 times fewer parameters than RFNet. We further propose a dynamic, energy-aware inference strategy that adaptively selects between these two models based on the device's remaining battery level, the model's prediction confidence, and the operational context. Extensive experimental evaluation on a dataset of 30 commercial LoRa devices demonstrates that this adaptive approach achieves an overall identification accuracy of 94.54% while reducing energy consumption by 17% compared to exclusively using the high-accuracy model. This system provides robust protection against physical-layer threats with minimal energy overhead, thereby extending the operational lifetime of devices in secure, ultra-low-power IoT deployments.