RFID Research Articles

Wearable Collar Technologies for Dairy Cows: A Systematized Review of the Current Applications and Future Innovations in Precision Livestock Farming

Wearable collar technologies have become integral to the advancement of precision livestock farming, revolutionizing how dairy cattle are monitored in terms of their behaviour, health status, and productivity. These devices leverage cutting-edge sensors, including accelerometers, RFID tags, GPS receivers, microphones, gyroscopes, and magnetometers, to provide non-invasive, real-time insights that enhance animal welfare, optimize resource use, and support decision-making processes in livestock management. This systematized review focuses on analyzing the sensors integrated into collar-based systems, detailing their functionalities and applications. However, significant challenges remain, including the high energy consumption of some sensors, the need for frequent recharging, and limited parameter coverage by individual devices. Future developments must focus on integrating multiple sensor types into unified systems to provide comprehensive data on animal behaviour, health, and environmental interactions. Additionally, advancements in energy-efficient designs, longer battery life, and cost-reduction strategies are essential to enhance the practicality and accessibility of these technologies. By addressing these challenges, wearable collar systems can play a pivotal role in promoting sustainable, efficient, and responsible livestock farming, aligning with global goals for environmental and economic sustainability. This paper underscores the transformative potential of wearable collar technologies in reshaping the livestock industry and driving the adoption of innovative farming practices worldwide.

Solar-Powered Automated Toll Collection System and Integrating Weight Sensor for the Safety of a Bridge

This research will explore an RFID-Based Smart Automatic Weight-Based Toll Collection System designed to address traffic issues and enhance transparency in toll collection. Our goal is to develop a digital toll collection system that is both time-efficient and automated. The research focuses on a weight-based toll collection system using radio frequency identification (RFID) technology. In this system, RFID tags mounted on vehicle windshields are scanned by RFID readers to retrieve embedded information, eliminating the need for manual ticket payments and toll fee collections by authorities. This system facilitates smooth data exchange between vehicle owners and toll authorities, leading to more efficient toll collection, reduced traffic, and minimized human errors. Additionally, the research includes a weight sensor to measure vehicle weight, blocking access to overweight vehicles.

Traceability of Surgical Instruments: A Systematic Review

Objective: This study provides a comprehensive global overview of surgical instrument traceability systems and accentuates their growing importance in healthcare. Background: Surgical instruments pose risks to patient safety, economic costs, logistical challenges, and environmental impact. The increasing focus on instrument traceability reflects its potential to address these issues. Methods: We performed a systematic review using PRISMA guidelines, analyzing articles from 2000 to 2023 across five digital libraries (PubMed, Web of Science, IEEE, ACM, Google Scholar). Our review concentrated on traceability systems’ lifecycle for reusable and sterile surgical instruments. Results: Out of 7189 articles retrieved, 22 were selected for evaluation, and only 6 were considered relevant after a thorough examination. These studies mainly deployed Radio Frequency Identification (RFID) technology. They enhance patient safety, reduce environmental impact, improve economic efficiency, and optimize logistics. Additionally, these systems encourage more responsible surgical practices. Conclusions: Our study underscores the limited applied research in this field and discusses system architectures and performance metrics. It proposes future research directions, including the development of public databases, integration of automation, and investment in artificial intelligence (AI) and computer vision to improve traceability and risk analysis.

Enhancing counterfeit RFID tag classification through distance based cognitive risk control

In the realm of radio frequency identification (RFID) systems, combatting counterfeit tags through anti-counterfeiting technologies has garnered significant attention, particularly physical layer identification methods, lauded for their cost-effectiveness and simplicity in deployment. Nonetheless, the performance of physical layer recognition method is significantly impacted by the conditions of the tag detection setting, especially in scenarios characterized by low signal-to-noise ratio (SNR), where classification accuracy tends to suffer. To tackle this challenge head-on, this study proposes the implementation of a cognitive risk control strategy, which fine-tunes tag distance within the tag classification process to bolster the SNR and enhance recognition precision. Beyond the enactment of cognitive risk control, this paper extends its focus to encompass enriched time domain and frequency domain feature extraction, totaling 104 features, aimed at further enhancing classification efficacy. Leveraging software-defined radio devices, classification experiments encompassing seven popular tag types from three distinct manufacturers were conducted. Results from these experiments reveal that upon integrating the cognitive risk control strategy, the average accuracy of tag classification experiences an approximate 11% increase. Concurrently, in comparison to traditional twenty-eight and seven features, the adoption of one-hundred-and-four features translates to an enhancement in classification accuracy by roughly 4.3% and 5.3%, respectively. These findings not only underscore the efficacy of cognitive risk control in elevating label classification accuracy within low SNR environments but also underscore the potential for augmenting classification performance through an increased feature set.

Design of a Miniaturized Dual-Band Antenna using Slotted Techniques for 2.45/5.8 GHz Microwave Band RFID Utilizations

In this study, a compact dual-band antenna for Radio Frequency Identification (RFID) readers operating at 2.45 GHz and 5.8 GHz is proposed. It is mounted on a FR4 substrate with a dielectric permittivity of 4.3. The proposed structure occupies a total area of 728 mm², with dimensions of 28×26 mm². The antenna features a rectangular radiating patch with integrated slots to enhance performance. A precisely tuned rectangular transmission line feeds the antenna, ensuring efficient signal coupling. The ground plane design incorporates a geometry derived from the slot technique, consisting of a rectangular loop, a square loop, and an attached rectangular element. This slot-based approach facilitates antenna miniaturization and enables the generation of two distinct resonant frequencies, significantly improving overall performance. The antenna was designed, simulated, and analyzed using the CST Microwave Studio (CST MWS) tool. The resulting structure is compact and straightforward, exhibiting suitable gain, coherent radiation patterns, and efficient impedance matching. Its design allows easy integration into handheld RFID readers operating in the microwave band.

Modification and Performance Evaluation of Radio Frequency Identification Technology in High-Temperature and High-Pressure Environments

Summary As oil and gas exploration progresses into deeper and ultradeep territories, the radio frequency identification (RFID) tags used in drillpipes are required to demonstrate enhanced resilience to elevated temperatures and pressures. Current research on tags for ultrahigh temperature and ultrahigh pressure environments is relatively scarce. This study focuses on the application of RFID in deeper drilling operations, aiming to further enhance the tags’ temperature and pressure resistance to meet the application requirements. Numerical simulations were conducted to assess the mechanical properties of RFID tags installed on drillpipes of varying dimensions under conditions of elevated temperatures and pressures. This process enabled the optimization of the dimensions of the RFID tags and installation holes. The optimal dimensions for the RFID tag were determined to be φ24.3×7 mm, with the corresponding installation hole being φ24.3×11 mm. To evaluate the high-temperature endurance of the RFID tags, they were subjected to a temperature of 200℃ for a period of time, after which a maximum temperature of 230℃ was applied. Furthermore, a bespoke high-temperature and high-pressure apparatus was used to conduct a 7-day endurance test at 200℃ and 200 MPa. The findings demonstrated that the RFID tags exhibited reliable performance even when subjected to an extreme temperature of 230℃. Furthermore, the RFID tags exhibited consistent performance when subjected to prolonged periods of high temperature and pressure. The efficacy of the RFID tags was further validated through an 11-well field experiment, wherein the embedded tags exhibited no indications of detachment or deterioration. Once removed from the apparatus, the tags were able to be read automatically in real time, thus enabling the automated collection of data from the drillpipe in actual operating conditions. The findings of this study provide valuable insights and establish a foundation for the design and application of RFID tags in the challenging environments of deep and ultradeep oil and gas exploration.

Passive RFID sensor based on loaded short circuit and parasitic unit for ammonia sensing enhancement

Passive RFID sensor based on loaded short circuit and parasitic unit for ammonia sensing enhancement

Novel design of a high-frequency radio frequency quadrupole accelerator for medical application

Proton therapy is an advanced particle radiotherapy technology. Lanzhou University recently designed a new linear accelerator system for proton therapy. In the system, a 750[Formula: see text]MHz radio frequency quadrupole (RFQ) accelerator was used to complete the initial acceleration of particles. The RFQ can accelerate a proton beam intensity of 1[Formula: see text]mA from 30[Formula: see text]keV to 3[Formula: see text]MeV kinetic energy within a length of 2[Formula: see text]m. This paper aims to complete the physical design of this high-frequency RFQ, including beam dynamics design, radio frequency (RF) design, and multiphysics analysis. In the dynamics design, a novel design strategy for controlling the longitudinal emittance was adopted to improve the output beam quality of the RFQ. The simulation results showed that the RFQ outlet longitudinal emittance was controlled below 0.1 [Formula: see text] mm mrad. In the RF design, the pi-mold rod structure was applied to RFQ at such high frequencies for the first time. After the whole cavity simulation, it was found that the separation between the operating frequency of the RFQ cavity and its closest dipole mode reached 58.3[Formula: see text]MHz, and its quality factor reached 6352. Finally, a multiphysics analysis was performed. The analysis showed that the maximum temperature rise of the RFQ cavity was less than [Formula: see text]C and the frequency drift due to heating was 18[Formula: see text]kHz. This paper presents the current status of the design of this new linear accelerator. Compared with previous RFQs, the new RFQ has a very high transmission efficiency and higher RF stability.

Changes in lake sturgeon spawning periodicity is associated with prior reproductive effort

Long-lived iteroparous organisms vary resource expenditures toward migration and reproduction in response to individual physical factors and conspecific interactions, which can affect future reproductive timing and interval. Reproductive actions can lead to trade-offs associated with allocations to current vs. future reproduction, including longer reproductive interval, require additional study. The objective of this study was to evaluate associations between physical stream characteristics, individual behaviors, and breeding demographics and spawning periodicity in lake sturgeon (Acipenser fulvescens). We used Radio Frequency Identification tags to monitor spawning migration by male (N = 1931) and female (N = 683) adults over seven consecutive years (2016 through 2022) in the Black River, Cheboygan Co., MI. We used ordinal regression models to quantify associations. Male spawning periodicity (1.60 ± 0.63 years; mean ± SE) decreased with increasing body size and intra-sex interactions and increased with increasing cumulative temperature, discharge, number of inter-sex interactions, and complete river migrations in a season. Female spawning periodicity (3.19 ± 0.05 years, mean ± SE) decreased with increasing upstream swimming time and inter-sex interactions. Results demonstrated spawning periodicity shortened as male lake sturgeon age, and future breeding opportunities decreased, while female periodicity may be more individualized and is more likely to be affected by resource acquisition.

Factors affecting the adoption of RFID in the food supply chain: a systematic literature review

The unsustainability of the current food system raises food security concerns worldwide due to the population’s increased demand for fresh food and food safety. Unsafe food incidents lead to a high risk of poverty and economic loss. This includes food waste, safety, and security during the sustainable food system process from farm production to consumer. There is a need to implement a fast traceability system like Radio Frequency Identification (RFID) in the sustainable food system to ensure food quality and safety, meet customer demands, and achieve Sustainable Development Goals (SDGs): 1—No poverty, 2—Zero hunger and 13—Climate action for 2030 target. The study objective was to explore the factors that affect the adoption of Radio Frequency Identification (RFID) in the food supply chain. The study adopted the TOE (technology-organization-environment) framework to explore factors that affect RFID adoption in the food supply chain. The study utilized a systematic literature review to examine the TOE factors influencing the adoption of RFID in the food supply chain. The results indicate that technological (complexity, cost, and security), organizational (technical skill and management support), and environmental (maintenance and support, IT policies and regulations) are the major factors that affect the adoption of RFID in the sustainable food system industry. The study recommends organisations intending to adopt RFID allocate enough resources and be prepared to overcome RFID adoption external challenges. The study concludes that technological factors, organizational factors, and environmental factors are important factors for RFID adoption in the food supply chain. However, further empirical studies are necessary to overcome the challenges of systematic literature review based on secondary data and convenience sampling.

Long-lived winter honey bees show unexpectedly high levels of flight activity compared to short-lived summer bees

In temperate regions, the overwintering success of honey bee colonies, Apis mellifera, depends on the last generations of long-lived bees emerging in autumn, known as winter bees. While the physiological qualities of winter bees and their extended lifespan are well documented, yet literature on their flight activity performance is scarce. Here we studied the flight activity of long-lived winter honey bees and compared their performance with short-lived bees. Using radio frequency identification (RFID), we automatically monitored the number of flights, the total flight duration and the lifespan of 523 honey bees that emerged over the course of a year, including short-lived bees from spring, summer and autumn, and long-lived winter bees. We found that flight activity performance of short-lived bees decreased progressively from spring to autumn for both the number of flights and the total flight duration. Furthermore, we confirm that only a fraction of the bees emerging before winter are long-lived winter bees, with a lifespan of 143.5 ± 23.5 days (mean ± SD). With an average of 37.5 ± 44.2 flights and 12.7 ± 15.5 h of flight, we found that long-lived winter bees were substantially more active than summer and autumn short-lived bees, but performed similar activity than spring short-lived bees. We also found that a small proportion of long-lived winter bees participate in the vast majority of the flight activity of the colony. Our results suggest that the extended lifespan of long-lived winter bees does not affect their flight activity performance, probably explained by their physiological qualities.

TagRecon: Fine-Grained 3D Reconstruction of Multiple Tagged Packages via RFID Systems

To meet the new requirements of Industry 4.0, the logistics field has introduced 3D reconstruction technology. Computer vision-based solutions face challenges like bad lighting conditions and line-of-sight constraints. Meanwhile, the widespread adoption of RFID tags in supply chains offers an opportunity to enhance current reconstruction methods. In this paper, we propose TagRecon, a fine-grained multi-object 3D reconstruction scheme utilizing well-deployed RFIDs. Specifically, TagRecon transforms the task of reconstruction into a problem of estimating 3D bounding boxes for tagged packages. By placing dual anchor tags on each target package, TagRecon enables accurate inference of the package’s translation and rotation using RFID-based localization and orientation sensing. Our scheme introduces a novel method to estimate rotations and translations for tagged packages, utilizing the known geometric relationship of anchor tags. Besides, to achieve simultaneous reconstruction of multiple packages, we manage to match tags from various packages through the correlation between anchor tag pairs. As far as we know, this is the first RFID-based solution that can simultaneously realize 3D translation and rotation estimation of multiple objects to a fine granularity. Experiments validate TagRecon achieves a 28.0 cm translation error and 6.8°, 6.0°, and 7.5° rotation errors for roll, pitch, and yaw angles on average.

Sustainable and Low-Cost Greenhouse Soil Moisture Monitoring Using Battery-Free RFID Sensors

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

Heart Rate Variability Estimation Based on RFID Tag-Pair in Dynamic Environments

With the rapid development of smart health care, accurate heart rate variability (HRV) estimation for the early detection of diseases has become a hot research topic. Advanced work uses the wireless signal to estimate the heartbeat in a contact-free way, which usually cannot separate multiple users or work in a dynamic environment. In this article, we propose a lightweight heartbeat-sensing method based on RFID tag pairs, which focuses on HRV extraction in a more general sensing scenario. Based on the tag-pair design, we build a novel heartbeat and respiration model to describe the signal relationship between the two tags from the time and space domains. Based on the model, we propose a Calibrated Temporal-Spatial IQ-Shaping-based signal cancellation algorithm to cancel the respiration and extract the heartbeat. To remove the interference in dynamic measurement, we build an IQ-based signal model via a Principal Component Analysis-based interference estimation. To reduce the statistical error in HRV extraction, we further design a neural network to predict the HRV index. We have implemented a system prototype in a real environment with COTS RFID devices. Extensive experiments show that our system can achieve a median RMSSD error of 7.51 ms, which satisfies the medical demand in HRV measurement.

Patient satisfaction with radio-frequency identification (RFID) tag localization compared with wire localization for nonpalpable breast lesions: the RFID trial

BackgroundMost breast cancers are detected at an early stage in which case conservative surgery is indicated. An accurate preoperative localization technique is essential for conservative surgery of non-palpable breast lesions. Currently, the gold standard technique is wire localization (WL). However, this technique has well-known drawbacks. Several wire-free techniques have been developed to overcome these drawbacks; one technique is localisation by Radiofrequency Identification (RFID). The purpose of this clinical trial was to assess the superiority of RFID tags (HOLOGIC) in terms of patient satisfaction, over wire localization of non-palpable breast lesions.MethodsThis was a single-centre, prospective, controlled and non-interventional trial. Patients were followed from their inclusion at the time of the preoperative consultation to the postoperative consultation, one month after surgery. Data on anxiety and satisfaction was collected from patients and clinicians using questionnaires, and clinical data was collected from the medical files. The primary outcome was the patients’ satisfaction scores, assessed using a visual analogue scale.ResultsEighty patients were sequentially enrolled in two groups: the wire group (n = 40) and the RFID group (n = 40). One patient from the RFID group was excluded from the analysis because of a substantial migration during deployment. On a 10-point Visual Analogue Scale, the patients’ median satisfaction score was 9.8 (IQR = 1.32) for the wire group and 10 (IQR = 0.07) for the RFID group (p < 0.001). A reduction in pain between device insertion and surgery was observed in the RFID group (p = 0.009). The median placement time was shorter in the RFID group (15 min, IQR = 6) than in the wire group (20 min, IQR = 30) (p = 0.01).ConclusionOur results show a statistically significant difference in median patient satisfaction score with the localization of non-palpable breast cancer lesions using RFID tags compared to the use of the WL. Although our results did not show clinically significant outcomes in terms of satisfaction, RFID tags are a reliable alternative to WL and simplify the organization of patients’ healthcare trajectories.Trial registrationClinicalTrials.gov ID; NCT04750889 registered on February 11, 2021. https://clinicaltrials.gov/ct2/show/NCT04750889?term=rfid&draw=2&rank=1

Measuring the effect of RFID and marker recognition tags on cockroach (Blattodea: Blaberidae) behavior using AI-aided tracking.

Radio frequency identification (RFID) technology and marker recognition algorithms can offer an efficient and non-intrusive means of tracking animal positions. As such, they have become important tools for invertebrate behavioral research. Both approaches require fixing a tag or marker to the study organism, and so it is useful to quantify the effects such procedures have on behavior before proceeding with further research. However, frequently studies do not report doing such tests. Here, we demonstrate a time-efficient and accessible method for quantifying the impact of tagging on individual movement using open-source automated video tracking software. We tested the effect of RFID tags and tags suitable for marker recognition algorithms on the movement of Argentinian wood roaches (Blapicta dubia, Blattodea: Blaberidae) by filming tagged and untagged roaches in laboratory conditions. We employed DeepLabCut on the resultant videos to track cockroach movement and extract measures of behavioral traits. We found no statistically significant differences between RFID tagged and untagged groups in average speed over the trial period, the number of unique zones explored, and the number of discrete walks. However, groups that were tagged with labels for marker recognition had significantly higher values for all 3 metrics. We therefore support the use of RFID tags to monitor the behavior of B. dubia but note that the effect of using labels suitable for label recognition to identify individuals should be taken into consideration when measuring B.dubia behavior. We hope that this study can provide an accessible and viable roadmap for further work investigating the effects of tagging on insect behavior.

A new framework to assess the impact of new IT-based technologies on the success of quality management system

Today, the ever-increasing growth of technology and data has undeniably affected various industries. Due to the importance of the factor of comprehensive quality management (QM), big data, and information technology (IT) in the industry and the increasing attention to it, different attitudes regarding the ways and means of reaching it have been presented by these sources. As a result, the purpose of this research is to study new IT-based technologies related to the success of the QMS. The statistical population of this research is the managers and employees of the technology sectors, which is equal to 400 people. The sample size was equal to 196 people, and a simple random sampling method was used to obtain the sample size. This research is applied in terms of its nature and purpose, and in terms of the data collection method, it is a descriptive and survey type. PLS structural equation modeling and SPSS software were used for data analysis. The findings showed that the internet of things (IoT), business intelligence (BI), cloud computing, and distributed systems have a significant impact on the success of the QMS, while near-field communication and radio frequency identification have an impact on the success of the QMS through the intermediary variable of the IoT. Finally, the results revealed that the expert systems also affect the success of the QMS through the mediator variable of BI.

Research on Challenges and Solutions for Optimizing Read Rate in RFID Systems

Radio Frequency Identification (RFID) uses electromagnetic fields for the automatic identification and tracking of object tags, which is widely used in supply chain management, transport and retail. And key issues in its application encompass reliability, security, and privacy. In contrast to mainstream research that primarily focuses on solutions for security and privacy, the paper concentrates on boosting the overall reliability of RFID systems, particularly in fault detection of read rates. Through the review and analysis of the relevant literature, this paper highlights the key challenges and issues in the RFID field, particularly focusing on read rate. Various methods proposed to improve RFID read rates are reviewed, including hardware optimization, tag design, software algorithm improvement, and environmental adjustments. In addition, it outlines the effectiveness of different strategies, addresses the challenges faced in real-world applications, notes the limitations of the research, and proposes potential directions for future studies. The results demonstrate that anti-collision algorithms such as DFSA and improved binary tree search can effectively reduce signal conflicts in multi-tag environments

Novel RFID Multi-Label Network Location Measurement by Dual CCD and Non-Local Means-Harris Algorithm.

To improve the performance of Radio Frequency Identification (RFID) multi-label systems, the multi-label network structure needs to be quickly located and optimized. A multi-label location measurement method based on the NLM-Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference. Then, a fast NLM algorithm that optimizes the kernel coefficient acquisition speed is used for image denoising. Finally, the Harris corner algorithm is used to obtain the corner points of the images. After screening the diagonal points, the pixel coordinates of the preset origin and the four corners of the labels are obtained. Furthermore, the actual coordinates of the labels are obtained according to the pixel relationship. The results show that the average absolute errors of x, y, and z coordinates are 0.773 mm, 0.782 mm, and 0.807 mm, respectively. In addition, the relative errors are 1.659%, 2.260%, and 0.258%, which shows the high location accuracy of the multi-label network. It is of great significance to measure and optimize the performance of multi-label systems.

The architecture design of emergency logistics management system based on the Internet of Things

The Internet of Things (IoT) aims to connect intelligent products with network functions through both wireless and wired technologies, enabling the safe and efficient distribution of services across global networks, including computers, mobile phones, and other electronic devices. The emergence of network devices with communication, sensing, and actuation capabilities since 2005 has led to a wide range of applications, such as inventory management, environmental monitoring, and tracking moving objects. However, the progress of IoT technology is hindered by the lack of adequate infrastructure capable of supporting trillions of devices. This paper explores the role of radio frequency identification (RFID) sensor networks within the IoT architecture for logistics management, positioning these networks as key “network-enabled” devices. The study investigates the components of the system, which include the domain sensor name server (DSNS), sensor service publisher (SSP), historical database, and RFID sensors. The strategic and operational implications of IoT architecture in logistics management are analyzed, with a particular focus on emergency response scenarios. The findings highlight the potential of IoT to improve resource allocation, enhance situational awareness, and support real-time decision-making during critical operations. This research offers valuable insights into the scalability challenges and the strategic benefits of IoT in logistics management, particularly in emergency contexts where timely data can significantly influence outcomes.