Chipless Radio Frequency Identification (RFID) tags are widely adopted due to their cost-effectiveness, lightweight design, and passive operation. However, their lack of computational capabilities makes them vulnerable to cloning and counterfeit attacks. This paper proposes a counterfeit detection framework that combines Differential Constellation Trace Figures (DCTFs) with machine learning techniques to address these challenges. Backscattered Time-Domain (TD) signals from seven identical chipless RFID tags were processed to generate DCTFs, which were enhanced using colormaps such as Turbo, Colorcube, and Prism to highlight subtle variations. Red, Green and Blue (RGB) color features were extracted across spatial (width and height), radial, and angular dimensions, forming a multi-dimensional dataset. Gaussian noise was added to simulate real-world conditions, with Signal-to-Noise Ratios (SNRs) ranging from 100 decibels (dB) to 0 dB. Machine learning models, Support Vector Machine (SVM) and Random Forest (RForest), were trained to classify authentic and counterfeit tags. RForest demonstrated superior performance, achieving an accuracy of 99.62% and Area Under the Receiver Operating Characteristic curve (ROC–AUC) of 99.18% with multi-dimensional input data at 70 dB SNR noise.

This article introduces a novel approach to address the security challenges of chipless tag systems in the context of Internet of Things (IoT) applications. The proposed technique focuses on preventing tag cloning by leveraging the inherent natural randomness in the fabrication process. A groundbreaking aspect of this research is the utilization of an affordable and portable Multiple Input Multiple Output (MIMO) antenna system in real-world scenarios to detect counterfeit tags. The study begins by validating the precision of the MIMO system, establishing its effectiveness compared to Vector Network Analyzer (VNA) equipment. Additionally, the clone detection system is thoroughly evaluated for accuracy, and improvements are introduced through various Machine Learning (ML) models. The ML techniques used were able to detect clones with 99.78% accuracy, outperforming VNA equipment. This research represents a significant advancement in chipless tag security for IoT applications, offering a cost-effective and efficient solution to the pressing security issue of tag cloning.

Accurate dielectric constant measurements are crucial in Internet of Things (IoT) sensing applications to characterize materials and their properties. This article introduces an innovative capacitor-based chipless radio frequency identification (RFID) sensory array tailored specifically for precise measurements of dielectric constants in IoT contexts. The array incorporates Pi-shaped resonators and cylindrical capacitors, addressing challenges, such as low accuracy, limited$\varepsilon _{r}$measuring range, and the reliance on bulky vector network analyzers (VNAs) as readers. Theoretical modeling, design considerations, sensor calibration, and validation processes are detailed, highlighting the array’s precision and adaptability. Real-world IoT applications are demonstrated, showcasing the array’s potential with a low-cost portable multiple-input-multiple-output (MIMO) reader, Walabot. This cost-effective solution overcomes conventional limitations, offering a versatile approach to dielectric constant measurements and opening up new possibilities for diverse IoT sensing applications. The integration of this sensory array with IoT technologies demonstrates the feasibility of smarter material characterization.

Owing to the combination of high-speed data transmission and ubiquitous coverage, the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) network (HLWNet) has been recently proposed as a promising scheme for the next generation indoor wireless network. The handover problem in the HLWNet, however, becomes critical, due to the small cell size of the LiFi access point and the line-of-sight propagation of the optical signal. To provide accurate and timely handover decisions for the HLWNet,$w$e regard the handover in HLWNet as a pattern recognition problem for the first time. In this paper, channel quality, optical channel blockage, user movement, and device orientation are characterized to model a practical simulation scenario. Two different pattern recognition techniques have been applied to design handover algorithms in the HLWNet. The simulation results show that the proposed handover algorithms are able to provide higher user throughput, lower handover rate, and better robustness performance as compared to benchmarks.

This article 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, Nonline-of-Sight (NLoS) 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 article 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 article 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 commercialization for IoT applications.

This article presents the concept of a chipless radio-frequency identification-based pervasive crack sensing scheme for structural health monitoring (SHM). This scheme includes the design of a novel “smart skin” sensor that can provide contiguous or nondiscretized detection of a structural deformation at any point on its surface. The proposed sensor can identify the growth and propagation of cracks in an area of a building structure. Smart skin sensor has a sensitive microwave structure made of cascaded novel split box resonators coupled to a coplanar waveguide (CPW)-based transmission line. This enables it to offer an uninterrupted crack detection along with the ability to detect multiple structural perturbations simultaneously. The proposed sensor can detect a crack width of as little as 0.5 mm having any orientation; namely, vertical, horizontal, and diagonal cracks. In addition to crack detection, the sensing tag can also provide a distinctive response for moisture ingress into the structure. The article illustrates the theory behind choosing the split box resonator in this sensing scheme followed by the sensor design. It also provides a thorough analysis of the sensor, based on the simulated and experimentally obtained results. Both of these results conform to each other very well, which lays the foundation to use machine learning as future work, in detecting random structural cracks. Such results incorporate many distinguishing features that enable an estimation of crack location and orientations using visual and machine-based classification approaches. The repeatability of the obtained results is also established through the experimental analysis.

High peak to average power ratio (PAPR) is a problem in orthogonal frequency division multiplexing (OFDM) based communication systems. High PAPR introduces nonlinearity due to limited dynamic ranges of analog components. In case of visible light communication (VLC) it is more severe due to limited working range of light emitting diodes (LEDs). Clipping is often the simplest way to solve this problem. But it comes at a price of added clipping noise component to the signal. Clipping noise restricts the minimum attainable bit error rate (BER). A meta-data based semi-deterministic time domain approach is studied to reduce the clipping noise at the receiver. Simulation results show that clipping noise can be reduced with a simple algorithm compromising data rate by a small margin. The proposed algorithm can be applied without any restrictions on the OFDM parameters.

We present the design of asymmetric circular split ring resonator as a chipless radio frequency identification (RFID) sensor tag design that can be deployed to measure multiple physical parameters wirelessly in ultrawideband frequencies. Secondarily, the proposed tag design can extend to be used as a chipless RFID tag with actual data encoded for product identification purposes. The proposed resonator design consists of four complementary split circular rings. Each gap between the rings acts as a capacitive sensor which is optimized to have a resonance with a high-quality factor. Each resonance can be considered as a sensor to deploy smart materials that are sensitive to different environmental physical parameters, which can be operated in wireless conditions. The fabricated chipless RFID sensor provides a higher ${Q}$ factor, aligned with the optimized simulation model. Chipless RFID sensors are comparatively low-cost, but they provide enormous design flexibility in diversified applications. The proposed design holds a great promise for sensing multiple physical parameters in a remote setting.

A novel approach to decode information in a chipless RFID tag using signal space representation (SSR) is presented. SSR represents 2bpossible tag signatures of a b-bit tag as linear combinations of a small set of L orthonormal basis functions. Each signature encoding a binary bit sequence is represented by a point in an L-dimensional constellation. Prototype 3-bit chipless RFID tags are used to validate the detection technique. The proposed method gives a solid mathematical framework to develop detection and decoding methods for more complicated tag reading scenarios.

In this paper, we present the low noise amplifier using new feedback connection configurations. The UWB LNA is design in 0.18 mum TSMC CMOS technique to achieve high gain, small size and low noise. The LNA achieved 11 dB of average power gain, low 2.87 dB noise figure (NF), -10.9 dB input match, -7 dB return loss, -3 dBm of IIP3 and only 0.54 mm2size with 15 mW power consumption.

We propose and compare two receiver structures that combine optimum receive beamforming, respectively, with the conventional Alamouti space-time block code (STBC) decoder and the minimum mean squared error (MMSE) decoder. The beamforming process suppresses co-channel interference (CCI) by maximizing the uplink signal to noise plus interference ratio (SINR). Co-antenna interference (CAI) is suppressed at the Alamouti STBC decoders. Simulation results show that both receivers significantly improve the bit error rate (BER) performance and increase the achieved channel capacities. The comparison of the two receivers indicates that, in the presence of beamforming, the conventional Alamouti decoder outperforms the MMSE decoder when there are sufficient degrees of freedom (DOF) at the receiver antenna array.