Passive Radio Frequency Identification Systems Research Articles

Review on Recent Advances and Applications of Passive Harmonic RFID Systems.

Radio frequency identification (RFID) has gained significant attention because it provides a highly versatile platform for identifying, tracking, and monitoring objects. An emerging trend in this technology is the use of nonlinear RFID, such as passive harmonic tags, which have been demonstrated to be effective against clutters, echoes, crosstalk, and other electromagnetic interferences. This article presents a comprehensive review of recent advances and applications of passive harmonic RFIDs and integrated systems. A passive harmonic RFID exploits the frequency orthogonality of the transmitted (fundamental tone) and received (harmonics) radio-frequency (RF) signals to enable robust interrogation in noisy and cluttered environments, not possible with traditional passive linear RFIDs. This review article evaluates passive harmonic RFID systems in comparison to traditional systems and highlights their pros and cons. Several state-of-the-art chipless and chip-based harmonic RFIDs are presented, and their novel applications in identification, tracking, sensing, and biotelemetry are discussed. The review summarizes the key successes and challenges of passive harmonic RFID systems and provides insights into their future development, implementation, and optimization.

Ultralightweight Cryptography for passive RFID systems

RFID (Radio Frequency Identification) is one of the most growing technologies among the pervasive systems. Non line of sight capability makes RFID systems much faster than its other contending systems such as barcodes and magnetic taps etc. But there are some allied security apprehensions with RFID systems. RFID security has been acquired a lot of attention in last few years as evinced by the large number of publications (over 3000). In this paper, a brief survey of eminent ultralightweight authentication protocols has been presented & then a four-layer security model, which comprises of various passive and active attacks, has been proposed. Finally, Cryptanalysis of these protocols has also been performed under the implications of the proposed security model.

Efficient Modeling of On-Body Passive UHF RFID Systems in the Radiative Near-Field

A novel method is presented to accurately determine the operational range of an on-body, passive ultrahigh-frequency (UHF) radio frequency identification (RFID) system operating in the radiative near field, based on its far-field radiation patterns. To this end, an efficient algorithm based on 3-D multipole expansion of the electromagnetic fields is formulated. By combining the new operator with the simulated or measured stand-alone far-field radiation patterns of the on-body RFID system, a comprehensive and accurate range determination is obtained. Compared with commercial software tools and measurements, we prove the accuracy and improved speed of the novel technique.

Experimental Results and Performance Analysis of a 1 × 2 × 1 UHF MIMO Passive RFID System.

Ultra-high frequency (UHF) multiple input multiple output (MIMO) passive radio frequency identification (RFID) systems have attracted the attention of many researchers in the last few years. The system modeling and theoretical performance analysis of these systems have been well investigated and revealed in many studies, yet the system prototype and the corresponding experimental results are scarce. In this study, measurements of a 1 × 2 × 1 UHF passive RFID system, including a MIMO UHF passive RFID tag prototype and its corresponding software-defined radio-based reader, taken in a microwave anechoic chamber, are presented. The experimental results are compared with theoretical values and computer simulations. The overall results demonstrate the consistency and the feasibility of UHF MIMO passive RFID systems.

Non-Invasive Monitoring of Respiratory Rate and Respiratory Status during Sleep Using a Passive Radio-Frequency Identification System

In recent years, checking sleep quality has become essential from a healthcare perspective. In this paper, we propose a respiratory rate (RR) monitoring system that can be used in the bedroom without wearing any sensor devices directly. To develop the system, passive radio-frequency identification (RFID) tags are introduced and attached to a blanket, instead of attaching them to the human body. The received signal strength indicator (RSSI) and phase values of the passive RFID tags are continuously obtained using an RFID reader through antennas located at the bedside. The RSSI and phase values change depending on the respiration of the person wearing the blanket. Thus, we can estimate the RR using these values. After providing an overview of the proposed system, the RR estimation flow is explained in detail. The processing flow includes noise elimination and irregular breathing period estimation methods. The evaluation demonstrates that the proposed system can estimate the RR and respiratory status without considering the user's body posture, body type, gender, or change in the RR.

A Data Augmentation-Based Method for Robust Device-Free Localization in Changing Environments of Passive Radio Frequency Identification System

Device-free localization (DFL) is playing a critical role in many applications which do not require any device attached to the target. Because of environmental changes, the fingerprint database established in the original environments is unable to remain effective when used in the changing environments. Looking at dropout as a prior-knowledge-free data augmentation operation, we propose a convolutional denoising autoencoder (DAE)-based DFL architecture AugRF. Our method combines the merits of the convolutional neural network and the DAE in which convolutional operation is beneficial to feature extraction and dropout operation is equivalent to generating augmented versions of the training data. It is noted that our method can improve the localization performance in changed environments without resampling and retraining processes. Simulations and real-world experiments verify the superiority of the proposed architecture. Our localization system can be implemented with a commercial ultrahigh-frequency radio frequency identification system. When the signal-to-noise ratio of the fingerprint data drops significantly due to environmental changes, AugRF contributes to improving localization accuracy. In addition, the time cost for one positioning meets the requirement of real-time indoor localization.

Passive UHF RFID Network Planning for Accurate 3-D Location via Restricted Genetic Algorithm

Indoor localization via radio-frequency identification (RFID) carries critical importance due to its high accuracy and low hardware requirement. The positions of reader antennas can affect the positioning accuracy and coverage in RFID network. In this letter, we present a novel RFID network planning (RNP) approach to optimize the deployment of reader antennas for accurate 3-D location. First, the 3-D antenna radiation mode of a passive UHF RFID system is set up. Then, the received signal strength (RSS) and path loss characteristics (PLC) are analyzed and a restricted genetic algorithm (RGA) is developed to obtain the optimal solution of the RNP via maximizing the total reward function with constraints. Finally, the convolutional neural network (CNN) and weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (WKNN) algorithms are respectively used to verify the localization effect. For comparison, the Cramér-Rao lower bound (CRLB) is also derived. The experimental results show that the proposed approach can improve the positioning accuracy as well as the coverage, and enhance the anti-noise capability of the location system.

A reliability and performance GSPN-Based model for anti-collision RFID algorithms under noisy channels in industrial internet of things

The Industrial Internet of Things (IIoT) has the potential to deeply change the technological landscape in factories, transforming the way automation procedures are performed and relevant objects are identified. For that, however, reliability and performance issues have to be considered when providing the expected communication services. When employing Radio-Frequency Identification (RFID) in an IIoT context, previous works have struggled to improve the efficiency of RFID-complaint systems, usually defining mathematical models for planning and quality assessment. Nevertheless, such models are designed based on error-free communications, which are unreal when considering the error-prone nature of wireless communications in industrial plants. Therefore, this article defines a new formal model based on Generalized Stochastic Petri Nets (GSPN) for RFID communications, modelling different possibilities for errors between readers and RFID tags. Since this proposal employs the EPCglobal UHF Class 1 Gen2 parameters as reference, which are already adopted by the Dynamic Frame Slotted Aloha anti-collision protocol for passive RFID systems, this model can be exploited to assess the reliability and performance of different RFID medium access protocols when assuming noisy channels, supporting better comparisons among different algorithms and protocols. Simulation scenarios are defined to present reliability and performance results when assessing RFID tags reading, which are valuable when designing and maintaining IIoT applications.

A dynamic data driven indoor localisation framework based on ultra high frequency passive RFID system

Better monitoring of workers' and the materials' flow within a production system can potentially enhance any facility's productivity and efficiency. This paper proposes a data driven framework to affordably localise indoor workers and materials using a passive radio frequency identification (RFID) system in large scale. Here, indoor wireless sensor networks are developed via passive Ultra-High Frequency (UHF) tags, where received signal strength indicator (RSSI) is measured by different access points (APs) to generate a fingerprinting database. Then, this database not only translates the signal strength reported by APs to distance through regression models but also helps to localise each tag utilising our proposed k-nearest neighbours (KNN) algorithm. Our improved KNN algorithm dynamically defines different neighbourhoods, in terms of size and topology considering environment status. Results from multiple experiments under different scenarios reveal that our proposed methods can detect and localise objects with an error as low as 0.36 m.

A dynamic data driven indoor localisation framework based on ultra high frequency passive RFID system

Better monitoring of workers' and the materials' flow within a production system can potentially enhance any facility's productivity and efficiency. This paper proposes a data driven framework to affordably localise indoor workers and materials using a passive radio frequency identification (RFID) system in large scale. Here, indoor wireless sensor networks are developed via passive Ultra-High Frequency (UHF) tags, where received signal strength indicator (RSSI) is measured by different access points (APs) to generate a fingerprinting database. Then, this database not only translates the signal strength reported by APs to distance through regression models but also helps to localise each tag utilising our proposed k-nearest neighbours (KNN) algorithm. Our improved KNN algorithm dynamically defines different neighbourhoods, in terms of size and topology considering environment status. Results from multiple experiments under different scenarios reveal that our proposed methods can detect and localise objects with an error as low as 0.36 m.

Indoor positioning of metal parts by fingerprinting using passive RFID

In a smart factory, location-aware functions are an important feature. An asset’s position provides the context for making decisions in the factory. In this study, we have proposed a technique for positioning metal parts using a passive RFID (Radio-frequency identification) system. We applied the fingerprinting method using RSSI (Received Signal Strength Indicator) values of the tag to estimate its position. The position was estimated with an accuracy of 89% in an area of 3.6x3.6 m. This provides a potentially more affordable means of tracking metal parts in a factory, with acceptable accuracy at lower costs than other indoor positioning techniques.

A collision window tree algorithm improved by fixed‐window and parallel‐matching schemes

In passive radio frequency identification systems, tag collision is a serious problem causing performance degradation. Most anti‐collision protocols waste numerous transmitted bits while eliminating or reducing the idle and collision slots. In this letter, an improved collision window tree (ICwT) algorithm is proposed by applying a parallel‐matching scheme to a fixed‐window scheme, in order to restrict the length of tag response as well as reduce the number of query cycles. As a result of the simulation, ICwT is proved to be more effective in terms of identification time and energy cost, compared to the existing tree‐based algorithms including collision tree, M‐ary query tree, dual prefix probe scheme, query window tree, and collision window tree.

Theoretical Performance Evaluation of UHF-RFID Systems With Multi-Antenna Maximum-Likelihood Decoding

Radio frequency identification (RFID) is a technique that enables autonomous identification of objects using tiny and cheap transponders (tags). Therefore, its use increases, especially in the area of logistics. However, in dense applications scenarios with many tags multiple tags may transmit their data at the same time, leading to collisions. These collisions, especially in case of UHF RFID, significantly reduce the system throughput. The use of multiple input–multiple output (MIMO) is one candidate to achieve a high throughput even in case of tag collisions. Within this paper, we therefore analyze the performance of maximum-likelihood decoding for UHF RFID with multiple reception antennas. We especially take into account the impact of the FM0 channel encoding used by the EPCglobal Class-1 Gen-2 standard, the most common UHF RFID standard in the area of logistics. The channel characteristics of passive RFID systems follow the so-called double or backscatter Rayleigh distribution, which makes the performance analysis a challenging task. We use two methodologies to analyze the performance in this. First, we derive an upper bound symbol error probability (SEP) based on the pairwise error probability (PEP). The second technique depends on the exact evaluation of the SEP by taking into account all probabilities of errors between symbols. We finally compare the accuracy of both methodologies with simulation results. These results show that the PEP analysis converges with increasing number of antennas, and the exact analysis gives accurate results even with only two receive antennas.

A Review of Passive Radio Frequency Identification Systems for Animal Monitoring in Livestock Facilities

Abstract. Animal facilities are increasing in size, while the availability of skilled workers is decreasing, thus, making it difficult for the farm laborers to ensure the health and well-being of all animals under their care. Passive Radio Frequency Identification (RFID) systems have been successfully used in animal facilities and research has identified potential applications in behavior monitoring for automated illness detection. While RFID signals range in frequency from 9 kHz to 5.8 GHz, the three most common frequencies are Low Frequency (LF, 125 kHz or 134.2 kHz), High Frequency (HF, 13.56 MHz), and Ultra-High Frequency (UHF, 865-868 MHz or 902-928 MHz). The objective of this article is to compare and evaluate the application of these three different RFID systems within large research facilities for livestock and poultry in terms of hardware characteristics, system design, and data processing and usage. Differences in tag construction, availability and cost are evident, but also basic differences in reader and antenna function, such as physics of communication, speed of detection, and anti-collision procedures exist. The systems have significant differences in reading ranges and are known to have varying influence of materials, especially water and metal, on the performance of the systems. However, the data streams, as well as methods of data processing and the creation of events (e.g., visits to a feeder), are similar for all systems. The characteristics mentioned do not necessarily identify an ideal RFID technology but reveal positive and negative aspects of each system. The three different RFID systems have been successfully applied in livestock and poultry facilities. Current research is focused on the utilization of the RFID data in prediction and decision models for illness, animal welfare, and management actions. Keywords: Behavior, Cattle, Frequency ranges, Health and welfare, Poultry, Swine, Transponder.

Tunneling RFID Tags for Long-Range and Low-Power Microwave Applications

Backscatter modulation in radio frequency identification (RFID) tags will potentially connect billions of tomorrow's devices to the Internet-of-Things. Current passive RFID systems have power constraints that limit RFID tag communication to short ranges, but these limitations can be overcome by employing reflection amplifiers. In this paper, we show that negative differential resistance devices, such as tunnel diodes, exhibit 27 dB more gain and 10 dB lower power consumption than state-of-the-art reflection amplifiers. Two 5.8 GHz prototypes using off-the-shelf tunnel diodes show reflection gains of 40 dB and 29 dB for a total biasing power consumption of 45 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> W and 39 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sub> W , respectively, at impinging RF power levels as low as -84 dBm. A 5.8 GHz RFID link of 23 m was achieved when transmitting only -14 dBm of effective isotropic radiated power from a transceiver with a sensitivity of -90 dBm.

Collision Detection and Signal Recovery for UHF RFID Systems

In this paper, we present a novel high-throughput anti-collision algorithm for passive ultrahigh-frequency (UHF) radio-frequency identification (RFID) systems. Our algorithm utilizes signal recovery techniques of collided tag signals to both recover tag communications and obtain an accurate count of all tags in the field. Passive UHF RFID systems are used for long-range passive communications for applications including supply chain management and electronic tolling. Anti-collision algorithms are used to ensure successful RFID tag communications due to the likelihood of multiple tags being in the field and attempting to communicate simultaneously. The limited on-tag functionality necessitates the use of simple anti-collision algorithms such as a dynamic frame slotted Aloha (DFSA) algorithm. With our novel collision detection and signal recovery anti-collision algorithm, the RFID reader can retrieve multiple valid communications from each collided slot in a DFSA-based anti-collision protocol, while our algorithm allocates an optimal number of slots resulting in more collided but recoverable slots and fewer empty slots. Our algorithm achieves a nearly 100% throughput improvement with an expected throughput of 0.85 compared with an expected throughput of 0.426 for a standard DFSA algorithm. The reader receiver with the proposed algorithm is implemented in a field-programmable gate array and the whole reader system is verified using the communication tests with commercial tags. According to the synthesized results in an SMIC 0.13- $\mu \text{m}$ CMOS technology, the collision detection and signal recovery module consumes about 135k GE. Note to Practitioners —Signal recovery methods are capable of extracting information from collided signals. In this paper, we introduce and analyze a signal recovery method based on the voltage histogram of the received signal. Both the original signals and the number of collided signals are recovered. The information of tags is used by our novel algorithm to increase the system throughput in DFSA-based anti-collision algorithms. The recovered signals allow our algorithm to directly obtain the tag identifier. By determining the number of collided signals, our algorithm is able to calculate an accurate estimate of the total number of tags in the reader’s field. With signal recovery and an accurate tag count, our algorithm is able to reduce the total number of slots needed to identify all tags, thereby increasing the throughput. We present a novel frame length optimization method that increases throughout by shrinking the number of slots, thereby intentionally causing collisions and reducing the total number of empty slots. In addition, we present a hardware implementation of our novel method that is suitable for integration into RFID readers. When the signal strengths of the tags in collision in the in-phase or quadrature path are not close to each other, the performance of our method is much better than that of the traditional method in which the collision signal is treated as ineffective and ignored.

IoT Localization for Bistatic Passive UHF RFID Systems With 3-D Radiation Pattern

Passive radio-frequency identification (RFID) systems carry critical importance for Internet of Things (IoT) applications due to their energy harvesting capabilities. RFIDbased position estimation, in particular, is expected to facilitate a wide array of location-based services for IoT applications with low-power requirements. In this paper, considering monostatic and bistatic configurations and 3-D antenna radiation pattern, we investigate the accuracy of received signal strength-based wireless localization using passive ultra high frequency RFID systems. The Cramer-Rao lower bound (CRLB) for the localization accuracy is derived, and is compared with the accuracy of maximum likelihood estimators for various RFID antenna configurations. Numerical results show that due to RFID tag/antenna sensitivity, and the directional antenna pattern, the localization accuracy can degrade at blind locations that remain outside of the RFID reader antennas' main beam patterns. In such cases optimizing elevation angle of antennas are shown to improve localization coverage, while using bistatic configuration improves localization accuracy significantly.

A novel-Q DFSA algorithm for passive RFID system

The tag detection ability of Passive Radio Frequency Identification (RFID) systems are critically challenged by the collision occurrence due to simultaneous responding tags during the identification process. The dynamic scheduling of the frame size governed by Dynamic Frame Size ALOHA(DFSA) process, by adjusting the frame lengths according to the size of tag population can avoid the collisions during the identification. However, the performance of DFSA majorly depends on the frame size selection policy which in previous studies was adopted to achieve the target of throughput maximization during a frame. This condition is obtained at the cost of equating the frame size up to the number of estimated tags responding during the time frame. This approximation enhances the throughput value but contributes to massive energy wastages as the frame lengths approach to a very large value in large tag population size. Therefore, it is essential to develop the new frame size estimation policy for DFSA achieving the aim of optimization between throughput and energy for improved time and energy performance. In this paper, we have proposed an EPC C1G2 standards based Novel-Q DFSA algorithm which optimizes the frame size accounting both the energy and the throughput. The combined throughput and energy trade-offs are measured through Energy–Time-Delay (ET) cost which is minimum for our proposed algorithm compared to the existing solutions. Furthermore, the Throughput-Time Delay product approves the stability in large population size making it suitable for numerous identification applications.

A Feasible Segment-by-Segment ALOHA Algorithm for RFID Systems

In the passive radio frequency identification systems, dynamic frame slotted ALOHA framework has been popularly deployed by the industry driven EPCGlobal C1G2 standard to solve tags collision problem, where tags collision is mainly caused by the mismatched frame length leading to simultaneous responding tags in one same time slot and one reader needs to continuously select the appropriate frame length for effectively identifying tags. Obviously, the throughput improvement comes at the expense of frequent adjustments leading to large computation load and consumption. In order to decrease the frame length adjustment times and catch hold of the satisfactory throughput, this paper proposes a segment-by-segment ALOHA algorithm, where one frame is composed of slot-segments and each slot-segment is composed of s L continuous time slots with three scenarios as collision occupant, empty occupant and singleton occupant. To count these three scenarios in n L slot-segments, the corresponding adjustment operations with exclusive estimator to deal with the unread tags is further introduced. Compared with the state-of-the-art ALOHA-based algorithm in slot-by-slot fashion, the proposed one dramatically decreases the frame length adjustment times and partly increases the identification speed up to 420 tags/s with the throughput around 36% which is very close to the theoretical maximum 36.8%.

A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications.

In recent few years, the antenna and sensor communities have witnessed a considerable integration of radio frequency identification (RFID) tag antennas and sensors because of the impetus provided by internet of things (IoT) and cyber-physical systems (CPS). Such types of sensor can find potential applications in structural health monitoring (SHM) because of their passive, wireless, simple, compact size, and multimodal nature, particular in large scale infrastructures during their lifecycle. The big data from these ubiquitous sensors are expected to generate a big impact for intelligent monitoring. A remarkable number of scientific papers demonstrate the possibility that objects can be remotely tracked and intelligently monitored for their physical/chemical/mechanical properties and environment conditions. Most of the work focuses on antenna design, and significant information has been generated to demonstrate feasibilities. Further information is needed to gain deep understanding of the passive RFID antenna sensor systems in order to make them reliable and practical. Nevertheless, this information is scattered over much literature. This paper is to comprehensively summarize and clearly highlight the challenges and state-of-the-art methods of passive RFID antenna sensors and systems in terms of sensing and communication from system point of view. Future trends are also discussed. The future research and development in UK are suggested as well.