Device Identification Based on Artificial Polarization Fingerprint Injection

Polarization fingerprint (PF) is a promising technique for low-cost Internet of Things (IoT) device identification, which has better performance compared to radio frequency fingerprint (RFF). However, due to the manufacturing technology improvement, PF and RFF suffer from critical drawbacks, especially the reduction in fingerprint differences among devices. To solve this problem, we propose an artificial polarization fingerprint injection (APFI) scheme based on convenient antenna surface slotting. First, we find slotting can produce frequencydependent variation in polarization, which increases the fingerprint difference. Subsequently, we proposed the polarization frequency gradient matrix as a classification feature, based on the characteristics of the injected fingerprints, which eliminates the spatial instability of the original PFs. Extensive simulations and experiments demonstrate that APFI improves the device capability more than 7 times compared with the original PFs under 99.5% identification accuracy.