Physical-Layer Detection and Security of Printed Chipless RFID Tag for Internet of Things Applications

Abstract

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.

Complex Natural Resonance-Based Chipless RFID Multi-Tag Detection Using One-Dimensional Convolutional Neural Networks

“…However, it is important to note that we did not further increase the frequency shifts beyond the breakdown point (Dσ = 60 MHz and Df = 180 MHz) because the results in Table 2 consistently demonstrated 100% accuracy at this threshold. To this end, Table 4 provides a comparison of the proposed chipless RFID detection technique with six existing techniques [3], [8], [13], [16], [17]. The techniques were categorized based on the number of bits, whether single-tag or multi-tag testing was conducted, and the reported accuracy.…”

“…In experiments using a single tag, accuracy was 100%. From the table, it is evident that most of the articles focused on single-tag testing and achieved high accuracy rates in the range of 97% to 100% [3], [13], [16], [17]. One notable article in [8] employed the space-time-frequency anticollision technique, which is the only existing technique that allows for a comparison of multi-tag detection.…”

“…For instance, reference [16] presents a method that combines neural networks with frequency responses and spectrograms using 2D CNNs for chipless RFID tasks. Additionally, a deep learning-based security model was proposed to provide a high accuracy of 93%, when classifying a cloned chipless RFID tag from a genuine tag, even in the presence of additive RF interference in real-time [17]. These innovative approaches demonstrate the growing interest in applying deep learning to chipless RFID systems, offering promising avenues for enhanced performance and expanded applications.…”

Deep-Learning-Assisted Robust Detection Techniques for a Chipless RFID Sensor Tag

“…These challenges include addressing the trade-off between time and frequency resolution, which can lead to inaccuracies in frequency characterization, as well as handling nonstationary signal behaviour and adapting to the dynamic characteristics of high-data-capacity tags [21], [22]. Furthermore, the SSR technique, which aims to determine the minimum distance between received frequency signature points and fixed reference points, also grapples with issues related to adaptability and robustness [23], [24]. This specificity can make deploying CRFID systems in various applications and environments challenging without extensive customization and optimization efforts.…”

“…To reduce the reading complexity, several studies have leveraged the pattern recognition capabilities of both Machine Learning (ML) and its subfield, Deep Learning (DL), to effectively detect different EM signatures and retrieve accurate tag information [23], [25]- [28].…”