Fingerprinting chipless RFID with a MIMO system for tag authentication in Internet of Things

This paper presents a new method to tackle the security issues of chipless tag systems in Internet of Things (IoT) applications. The strategy aims to prevent the cloning of tags by utilizing the intrinsic natural randomness in the manufacturing process. This research presents a novel approach to generate fingerprints for chipless Radio Frequency Identification (RFID) tags using the unique backscattered electromagnetic (EM) responses, caused by inherent natural variations in resonator geometry, captured by a portable Multiple Input Multiple Output (MIMO) reader. Using twenty-two tags, one authentic and twenty-one counterfeits, two separate sets of fingerprints were generated using both Frequency Domain (FD) and Time Domain (TD) data, respectively. The clone detection model achieved an accuracy of 98.41% in a 35 dB noisy environment using fingerprints generated from FD data and 92.07% in a 50 dB noisy environment using fingerprints derived from TD data. This was obtained by utilizing similarity metrics such as Mean Squared Error (MSE) and Structural Similarity Index (SSI), offering a robust alternative to traditional tag authentication methods. The portability and affordability of the MIMO reader, combined with new opportunities for image-based identification, position this approach as a substantial advancement in the realm of authenticity verification for chipless RFID tags in the realm of IoT.