<div>This paper has proposed detection and physical layer security provision for printed sensory tag systems for internet of things (IoT) applications. The printed sensory tags can be a very cost-effective way to speed up the proliferation of the intelligent world of IoT. The printed Radio Frequency Identification (RFID) of a sensory tag is chipless with the fully printable feature, non-line-of-sight reading, low cost, and robustness to the environment. The detection and adoption of security features for such tags in a robust environment are still challenging. This paper initially presents a robust technology for detecting tags using both the amplitude and phase information of the frequency signature. After successfully identifying tag IDs, the paper presents novel physical layer security using a deep learning model to prevent the cloning of tags. Our experiment shows that the proposed system can detect and identify the unique physical attributes of the tag and isolate the clone tag from the genuine tag. It is believed that such real-time and precise detection and security features bring this technology closer to commercialisation for IoT applications.</div>
An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks
