Radio frequency identification (RFID) systems are emerging platforms that support a variety of pervasive applications. RFID tags can be used to label items and enable item-level monitoring. The ...problem of identifying missing tags in RFID systems has attracted wide attention due to its practical importance (e.g., anti-theft). This paper presents P-MTI: a Physical-layer Missing Tag Identification scheme that effectively makes use of the lower-layer information and dramatically improves operational efficiency. Unlike conventional approaches, P-MTI looks into the aggregated tag responses instead of focusing on individual tag responses and extracts useful information from physical-layer collisions. P-MTI leverages the sparsity of missing tag events and reconstructs tag responses through compressive sensing. We prototype P-MTI using the USRP software defined radio and Intel WISP platform. We also evaluate the performance of P-MTI with extensive simulations and compare to previous approaches. The experiment results show the promising performance of P-MTI in identification accuracy, time efficiency, as well as robustness over noisy channels.
The detection of a chipless radio frequency identification (RFID) tag in stepped motion is proposed. The performance is evaluated through experiment and measured using the vector network analyzer. ...The postprocessing of the measured results is performed using MATLAB. A novel adaptive wavelet-based detection algorithm is used for the decoding of the tag ID. An error analysis is conducted on the experimental results to study the variation of magnitude and frequency due to the movement of a chipless RFID tag. As far as the tag signature has not been disappeared from the reading range it has the same confidence level for the detection of its tag ID. This opens a new avenue toward developing the detection algorithms and decoding techniques when the chipless RFID tag is in motion. It also opens the doors for the localization of moving chipless RFID tags and prediction of the tagged objects moving trajectory modeling in the future.
In the Internet-of-Things era, it will be increasingly important to accurately and efficiently locate an object in the real world as well as identify it in the virtual world. However, it is not easy ...to accurately locate an indoor target using radio technology because the multipath propagation of radio waves in an indoor environment may lead to serious position estimation errors. In addition, when each target has a transceiver or each reader operates in its high-power mode, the overall power consumption of the whole system is considerable. In this paper, a dual-channel low-power passive RFID positioning system is proposed to solve this problem. The probability for accurately locating a target within 0.5 m from its real position can reach 96.7% in this system. The positioning area of this work is bigger than those of the prior arts. The total RF radiation power of one block of the proposed system is 23.14 dBm, which is the lowest among reported RFID positioning systems. Furthermore, this proposed architecture can be easily expanded to a large system.
This work demonstrates a novel approach for reliable and robust identification and detection of realized chipless RFID Arabic alphabets using deep learning (DL) method. The undertaken classification ...problem of Arabic RFID tags of various fonts and sizes requires a classification technique that can learn long-term dependencies. Hence, a Bi-Long Short-Term Memory (BiLSTM) model is developed to classify 28 chipless Arabic RFID letters of different font types and sizes using their back scattered dual-polarized radar cross section (RCS) characteristics. The RCS frequency response of each Arabic letter tag reflects its signature electromagnetic characteristics that vary with the change in its shape (variations in font type and size). Firstly, an RCS dataset of 28 Arabic alphabet tags with three different font types (Arial, Calibri, and Times New Roman) and 13 different font sizes (16 mm–28 mm with a step size of 1 mm) are generated using Finite-Difference Time-Domain (FDTD) method in the frequency range of 1–12 GHz (1001 steps). The dimensions of the resulting dataset are 28 (letters) × 13 (font sizes) × 1001 (frequency steps) × 2 (polarizations) × 3 (font types). Multi-class classification of the frequency-series data of all realized 28 alphabet tags of various font types and sizes makes the problem challenging and novel. The developed BiLSTM model can accurately classify the particular letter tag with specific font type and size based on the optimized network with employed Leave-One-Out Cross-Validation (LOOCV). The achieved accuracy with only Arial ((28 × 13 × 1001 × 2)), Calibri ((28 × 13 × 1001 × 2)), Times New Roman ((28 × 13 × 1001 × 2)), and combined data set ((28 × 13 × 1001 × 2) × 3) is 75%, 74%, 75%, and 89% respectively. The proposed Bi-LSTM model is shown superior when compared to other methods such as SVM, decision trees, and KNN, as it classifies the data with much higher accuracy for the considered multi-class data. The obtained accuracies of the compared models are 6.4% (SVM), 17.30% (tree) and 27.4% (KNN) respectively, while the developed Bi-LSTM model with optimized hyperparameters achieved an accuracy of 96%.
The radio frequency identification (RFID), is a wireless technology system that is used for identifying an individual or objects through the means of radio waves that transfer information from an ...electronic tag, called an RFID tag. RFID consists of two main components the interrogator and the transponder. The Interrogator, which is the RFID reader, the interrogator usually transmits and receives the signal while the transponder that is the tag, is attached to the object. In the RFID system, an RFID reader interrogates the RFID tags. This tag reader generates a radio frequency interrogation, which communicates with the tags been registered in the system. This reader likewise has a receiver that captures a reply signal generated from the tags and decodes the signal. This reply signal from the tags reflects the tag's information content. Each tag of the employee or student consists of a unique identity, identification card (ID) that is assigned to a single employee or student ID card, which is recorded, in the database of the system. This research reviews some recent designs and implementation of internet of things (IoT) attendance systems using the concept of the RFID system. The analysis found that the RFID system is a very advanced technology for an automatic attendance system in an institution, organization, or university and it provides a very higher performance and accuracy than the traditional paper-based system that the employees or students normally used to sign. The use of the RFID technology enables the institution, authorities, or management to evade attendance documents from damages such as misplacement, tear, or even got lost. A combination of the model is needed which will confirm higher security, better performance, and consistency of the system.
Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior ...by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that ShopMiner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference.
Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-tag localization, in which each object is attached with one tag. In this paper, we propose ...a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader's antenna spacing and placement; 2) supporting accurate calibration of the reader's antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader's antenna geometry does have impact on tag positioning performance.
RFID (radio frequency identification) technology appeared nearly 70 years ago. Deployed more widely only from the early 2000s, it is now booming and its development is still accelerating. As its name ...indicates, its original function was the identification (of objects, animals, people) and its applications were then essentially aimed at traceability, access control and logistics. If this type of use is still relevant today with more and more new application contexts and more and more efficient RFID tags, RFID has also evolved by integrating new capabilities. These new tags, known as augmented tags, include an information capture function. With the explosion of connected objects and the emergence of the Internet of Things (IoT), this old technology that is RFID still has a promising future and will probably be more and more present in our private and professional environments in all fields: logistics, industry, agriculture, building, health and even space.
Radio-Frequency IDentification (RFID) devices and sensors are among the main innovations of the last years, with an enormous impact on the Internet of Things (IoT) physical communication layer as ...well as on logistics and robotics. The aim of the present paper is to review the main technologies available for RFID sensors, and to identify the corresponding state-of-the-art when these technologies are applied to realistic IoT scenarios. Firstly, the concepts of radio backscattering and harmonic backscattering are analyzed, highlighting the pros and cons of each approach. Then, state-of-the-art solutions are reported, and the performance of each of them are discussed, to provide an overview of the potential of RFID-based sensing in different scenarios.
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