RFID Research Articles

Metasurface loaded twin-port microstrip antenna with circular polarization features for UHF RFID applications

Abstract This communication designs and investigates a twin-port microstrip antenna loaded with a metasurface (MS). The proposed MS works in two ways: (i) artificial ground plane for radiator and (ii) as an ideal absorber of normal incoming waves at the same time. A stair shaped slot is loaded on rectangular patch for creating circularly polarized wave in between 835–894 MHz. Antenna port slots oriented in a mirror manner increase isolation by 25 dB. To act as a validation tool, the suggested antenna was built and its executions in individually operating modes evaluated by statistical and experimental analysis. It is shown that combining the patch and absorber results in a well-matched antenna in between 710–980 MHz with improved gain (more than 3.0 dBi). The proposed antenna design effectively used for UHF (Ultra High Frequency)radio-frequency identification (RFID) applications, which is due to its ability to mitigate multipath reflection issues and incorrectRFID reading.

Foraging behavior and work patterns of Bombus terrestris (Hymenoptera: Apidae) in response to tomato greenhouse microclimate

Abstract Bumblebees play a significant role as pollinators for many wild plants and cultivated crops, owing to their elongated proboscis, resilience to diverse weather conditions, robustly furred bodies, and their unique capacity for buzz-pollination. To better understand the effect of greenhouse microclimates on bumblebee foraging behavior and working modes, a long-term record of foraging activity for each Bombus terrestris L. (Hymenoptera: Apidae) forager was monitored by the Radio-frequency identification system. The pattern of task performance, including constant housing, foraging, and day-off rotation, was examined under the microclimate. In addition, the correlation between foraging activity of bumblebees and temperature, relative humidity, illumination in the greenhouse, and pollen viability of tomato plants was further analyzed. Our findings revealed that B. terrestris can respond to microclimatic factors and plant resources while also exhibiting a suitable working pattern within the colony. Day-off rotation was observed as a strategy employed by foragers to prolong their survival time. This division of labor and task rotation may serve as strategies for the survival and development of the colony. Our research may contribute to fully understanding how microclimate and plants influence pollinator behavior within greenhouses, thereby optimizing the pollination management of bumblebees on greenhouse crops.

Developing a digital management system for museum collections using RFID and enhanced GIS technology

In recent years, the integration of Radio Frequency Identification (RFID) technology with deep learning has revolutionized the Internet of Things (IoT), leading to significant advancements in object identification, management, and control. Museums, which rely heavily on the meticulous management of collections, require precise and efficient systems to monitor and oversee their valuable assets. Traditional methods for tracking and managing museum collections often fall short in providing real-time updates and ensuring optimal environmental conditions for preservation. These shortcomings place a considerable burden on museum staff, who must manually track, inspect, and maintain extensive collections. This study addresses these challenges by proposing an advanced electronic management system that leverages the synergy between RFID technology and Geographical Information Systems (GIS). By integrating an enhanced LANDMARC algorithm into our geoinformation framework, the system visually represents the real-time location of museum collections on custom electronic maps, significantly improving the accuracy and timeliness of environmental monitoring. Additionally, RFID technology is utilized to continuously identify the real-time location of museum staff, facilitating the evaluation of their inspection tasks. This dual approach not only enhances the operational efficiency of collection management but also supports the development of intelligent, automated systems for museums, advancing the application of RFID technology in item identification and location management.

Individual return patterns of spawning flannelmouth sucker (Catostomus latipinnis) to a desert river tributary

Tributaries provide temporal and spatial habitat heterogeneity in river networks that can be critical for parts of the life history of a species. Tributary fidelity can benefit individual fish undergoing spawning migrations by reducing time and energy spent exploring new areas and leveraging previous experience, but anthropogenic activities that fragment or degrade these systems can eliminate those benefits. We used multistate models based on passive integrated transponder (PIT) detection data from 2013 to 2023 to estimate the proportion of flannelmouth suckers (Catostomus latipinnis) migrating to a tributary, McElmo Creek, from the mainstem San Juan River for spawning. Survival varied among years and among states. The top model for migration probability included sex, with males slightly more likely to migrate (0.93 vs 0.90), and the next model identified interannual variation in migration probability ranging from 0.875 to 0.999 across years, indicating high site fidelity. Individuals showed consistency in relative arrival timing across years, with the highest correlation generally during years with greater spring discharge and extended tributary residence time. Successful tributary spawning may be important for the maintenance of the mainstem San Juan River flannelmouth sucker population, but site fidelity may be maladaptive where tributaries are vulnerable to human alterations.

Revolutionizing Freight Management: A Synergistic Approach Using IoT and Blockchain Technologies

This article explores the transformative potential of integrating Internet of Things (IoT) and blockchain technologies in freight tracking and management. Traditional freight tracking methods often suffer from inaccuracies, delays, and lack of transparency, leading to inefficiencies across the supply chain. Integrating IoT devices, such as GPS trackers, RFID tags, and condition monitoring sensors, with blockchain's secure and immutable ledger system offers a promising solution to these challenges. This article examines the current state of freight tracking, the fundamentals of IoT and blockchain technologies, and their combined application in creating real-time, secure, and transparent freight management systems. It discusses the benefits of this integrated approach, including enhanced visibility, improved efficiency, reduced disputes, and more agile supply chain operations. The article also addresses the challenges and considerations in implementing such systems, including technical hurdles, data privacy concerns, scalability issues, and regulatory requirements. Finally, it explores future prospects and research directions, such as the integration with AI and machine learning and the potential for expanding applications beyond tracking to areas like autonomous logistics. Through a comprehensive analysis of current implementations and future possibilities, this article provides valuable insights into how IoT-blockchain integration is poised to revolutionize the freight industry, offering a roadmap for more efficient, secure, and transparent global supply chains.

A systematic scanning method to locate cryptic terrestrial species

When studying wild animals, consideration must be given to potential detrimental effects of the study technique, particularly if techniques may affect behaviour or energy expenditure. Many small terrestrial species occupy cryptic habitats, the characteristics and locations of which may be poorly understood. To study these habitats, researchers must be able to locate them, but must also consider the potential for disturbance of the organisms and the impacts this may have. Here, we developed and tested a novel, non-invasive method of locating the cryptic hibernation nests of passive integrated transponder (PIT) tagged hazel dormice Muscardinus avellanarius. The use of a powerful PIT tag scanner combined with a systematic search technique resulted in the location of nine wild hibernating dormice. Camera trap recordings indicated no external dormouse activity following detections, indicating minimal disturbance. In addition, eleven PIT tags no longer inside a dormouse were detected on the forest floor during searches. This study demonstrates a non-invasive alternative to techniques such as radio-collaring for small mammals, and highlights potential uses of PIT tags in research beyond identification of individuals, particularly in understanding fine-scale habitat selection.-A systematic search method enabled location of cryptic terrestrial species-The use of PIT tags allows detection with minimal disturbance

Conceptualization and standardization of a non-invasive closed head injury model using directed shockwave to mice

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide, with closed head injury (CHI) being one of the most common forms of TBI. Preclinical modeling of TBI is challenging due to confounding factors like craniectomy and poorly controlled injury severity. This study proposes a non-invasive CHI model using directed shockwaves. The mice heads were exposed to the shockwave and accommodated together following the implantation of RFID tags for automated neurocognitive assessment. Following a 13-day paradigm, mice underwent a digital gait analysis and subsequent classical behavioral test paradigms for affective, cognitive, and locomotor functions. Qualitative and quantitative histopathological assessment was carried out for shockwave pulses-dependent changes in terms of lesion volume, neuronal death, dendritic complexity, and spine density. Studies showed shockwave pulses-dependent differences in survivability, righting reflex, neural damage, and death. Shockwave-exposed mice showed significantly impaired learning and cognitive flexibility. Interestingly, exposed mice showed locomotor hyperactivity and risk-taking behavior (lack of anxiety) along with depression-like phenotypes. Our result suggests that the shockwave-based CHI models result in the clinically relevant phenotype and are precisely controlled for reproducibility.

Review and analysis on numerical simulation and compact modeling of InGaZno thin-film transistor for display SENSOR applications

Recent years have seen an increase in the use of Organic Thin Film Transistors (OTFTs), with applications ranging from flexible, low-cost displays to organic memory, RFID tag components, low-cost electronic appliances, and polymer circuits and sensors. Thin-film transistors (TFTs) have developed into a critical business on the grounds levels to their wide scope of utilizations in display; Radio-Frequency ID labels (RFID SENSOR), intelligent computation, and different areas. Reduced models are basic in the turn of events and execution of TFTs on the grounds that they overcome any barrier between the manufacture cycle and circuit plan. The motivation behind this exploration is to assemble a hypothetical structure for nanoscale TFT models made of polysilicon, indistinct silicon, natural, and In-Ga-Zn-O (IGZO) semiconductors. Extraordinary consideration is paid to surface-expected based smaller models of silicon-based TFTs. Surface-potential-based compact models and parameter extraction approaches were presented based on our knowledge of charge transport characteristics and TFT needs in organic and IGZO TFTs.

Inovasi Parkir Cerdas: Prediksi Posisi Kendaraan dengan Teknologi RFID dan Arduino Mega 2560 R3

Urban parking management is an urgent challenge in an ever-increasing vehicle mobility era. This research develops an intelligent parking system that integrates RFID (Radio-Frequency Identification) technology and Arduino Mega 2560 R3 to predict the vehicle's position in real time. The initial literature study identified various approaches to parking management, but none comprehensively combined RFID and Arduino Mega 2560 R3. Therefore, this study fills this gap. The research method involves literature study, system design, hardware and software development, and field testing. In the proposed system, vehicles are equipped with RFID cards, and the parking infrastructure is equipped with RFID readers. When the vehicle enters the parking area, the system reads the RFID card and predicts available parking positions. The results of field tests show an increase in the efficiency of parking space use of around 37% to 46%, and the accuracy of parking position prediction is above 90%. This research provides effective solutions for urban parking management, reducing traffic congestion and air pollution. This system also increases the driver's comfort in finding a suitable parking space. Thus, this research has the potential to support more sustainable urban development by optimizing the use of parking lots and reducing the negative impact of motorized vehicles in urban areas.

스마트 축사내 상황인지 자율이동형 환경센서 개발 및 가축행동 분석에 관한 연구

This study aims to develop a system that predicts the health status of cattle based on behavior patterns and environmental data within a smart barn using an autonomous driving system. Maintaining a unique ID for each cow using only a camera, without external devices (such as RFID tags), is essential. This enables the tracking of behavior patterns such as standing, sitting, and lying for each cow over time. Additionally, environmental data such as temperature and humidity are integrated to comprehensively assess the cows' health conditions. To achieve this, we propose a unique ID retention algorithm that combines object detection using YOLO, tracking with Deep SORT, and re-identification (ReID). Experimental results show that the YOLO + Deep SORT + ReID algorithm delivers the best performance in maintaining unique IDs, and the LSTM-based behavior analysis model demonstrates high accuracy in predicting behavior patterns. This system can serve as an effective tool for real-time prediction of livestock health conditions, such as disease or stress, through comprehensive analysis of environmental data and behavior patterns inside the barn.

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Numerous devices, including sensors, RF ID, and other types of smart devices, have been developed as a result of the Artificial Intelligence (AI), and Internet of Things (IoT) revolution. Urban areas can become smart by monitoring and collecting data about their surroundings through the deployment of technologies with powerful computational capabilities and those that are converted into intelligent things. Waste management is among the most significant issues in smart cities, a rise in metropolitan regions and faster increases in population are the main reasons. When it comes to gathering data about waste management, intelligent services can serve as the front line. Waste management with IoT support is a common example of a service offered by smart cities. Various duties, like gathering, processing, and use of waste in appropriate facilities, are included in waste management. The present study proposed an updated waste management system architecture design after reviewing existing artificial intelligence and IoT-based waste management systems and automation in smart cities. The proposed system architecture deals with the automation of municipality trash in smarter urban areas, using IoT technology and sending notification messages based on sensor data relating to the dustbin state, such as full or empty. The notifications are sent simultaneously to the municipality office and the waste carrier vehicle driver, so that waste can be emptied on time. The proposed system architecture represents a scalable and adaptable model for municipalities that aim to transform their waste collection processes and play a key step in minimizing municipality waste in smart cities. By deploying this proposed system architecture with smart sensors and IoT devices, municipalities can monitor waste levels to ensure that bins are emptied when it is necessary. This reduces the frequency of waste collection, lowers fuel consumption, and minimizes operational costs. The Route optimization algorithms further enhance efficiency by determining the most efficient paths for waste collection trucks, so they can reduce travel time and fuel emissions.

Evaluating the Efficiency of a Fishway Installed Near a High, Artificially Created Waterfall

ABSTRACTThe installation of fishways is the most common method to restore connectivity and allow fish to carry out their life cycle. However, the performance and efficiency of fishways are still highly variable, particularly for freshwater potamodromous species. We aimed to determine the passage efficiency of a fishway installed in 2021 downstream of the Coo waterfall in Belgium to allow upstream migration and crossing of the 11.8 m height. We double‐marked 38 individuals (RFID tag and radio transmitter) belonging to three species (Barbus barbus, Salmo trutta and Thymallus thymallus) from upstream and then released them 1.2 km downstream of the waterfall. A total of five automatic detection antennas were installed downstream of the waterfall and within the fishway, and the individuals were tracked with manual radio telemetry. We used several behavioural metrics to assess efficiency and attractiveness. The results indicate a lack of attractiveness of the fishway (overall rate of attraction < 25%). There was a higher detections at the waterfall (26 detections) than at the restitution channel (12 detections), where the entrance of the fishway was located. For individuals that reached the fishway entrance, the fishway efficiency was 12.5% for barbel and 6.3% for trout, with an average fishway entrance searching delay of 25 days for barbel. The lack of attractiveness led to numerous back‐and‐forth movements by individuals to find the entrance and the search for a substitute spawning habitat downstream. Our results indicate the need to improve the attractiveness of the fishway, in particular by improving the attraction flow.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Implementation of the Telemetric Integration of the BIM-RFID in Context of Access Control

Building Information Modelling (BIM) integration with the Internet of Things (IoT) is progressing. The high level of BIM maturity involves using sensor data to manage processes or objects. The article presents the process of creating a telemetry connection between the BIM model and a Radio-Frequency Identification (RFID) sensor in the context of gaining access to various parts of a building. The process of creating a connection using an experimental set based on a microcontroller board for RFID reader support is described. The set was programmed using multiple programming languages and artificial intelligence. The article presents a unique process of connecting an RFID reader with BIM using a simple model that can be replicated in other contexts (e.g., gaining access to different parts of a construction site). Previous research shows that the unidirectional connection of IoT sensors with BIM models is not difficult. Instead, real-time bidirectional and stable connection (telemetry) is problematic. The authors undertook to fill this research gap using a proprietary IoT kit, programming sequence, and lightweight communication protocol. The paper makes a significant contribution to the discussion and understanding of BIM-IoT technology integration. The article also includes the limitations and possibilities for further development of such a connection.

Research on the Storage Process Management System Based on RFID

With the continuous development and complexity of the global supply chain, warehouse automation and intelligent technology have become the key means to improve the efficiency and accuracy. The application of radio frequency identification (RFID) in storage process management is discussed, Use RFID technology to manage the storage process, pretreatment the goods, and improve the overall logistics speed. At the same time, the visualization technology is used to monitor the status of the goods, The full state of cargo preprocessing to the end of the task is visible.

Electromagnetic wave-driven deep learning for structural evaluation of reinforced concrete strength

Monitoring the performance of reinforced concrete structures, particularly in terms of strength reduction, presents significant challenges due to the practical limitations of traditional detection methods. This study introduces an innovative framework that incorporates a non-destructive technique using electromagnetic waves (EM-waves) transmitted via Radio Frequency Identification (RFID) technology, combined with two-dimensional (2-D) Fourier transform, fractal dimension analysis, and deep learning techniques to predict reductions in structural strength. Experiments were conducted on three reinforced concrete beam (RCB) specimens exhibiting various levels of reinforcement corrosion. From these, a dataset of 1,800 EMwave images was generated and classified into “normal” and “reduced strength” categories. These categories were used to train and validate a Convolutional Neural Network (CNN), which demonstrated robust performance, achieving a high accuracy of 0.91 and an F1-score of 0.93 in classifying instances of reduced structural strength. This approach offers a promising solution for detecting strength reduction in reinforced concrete infrastructures, enhancing both safety and maintenance efficiency.

Drone-based warehouse inventory management of perishables

Warehouse inventory management is a complex process. When inventory includes perishables, the complexity of these processes is compounded with additional requirements such as appropriate ambient storage conditions and placement of one type of perishables (e.g., bananas) far away from another type of perishables (e.g., strawberries). With perishables spending a significant amount of time post-harvest in warehouses, appropriate management of warehouse inventory is necessary to reduce wastage due to spoilage. Drone-based warehouse inventory management is gaining popularity as seen in the increasing number of firms in this space as well as the number of research publications. RFID tags have been widely used for inventory management for more than two decades. While drones have been successfully used in warehouses with non-perishables, RFID and drone use in warehouses with perishables has not witnessed its fair share as evidenced by the lack of publications in this general area. This paper is a step in the direction to address this void in published literature. We consider object-level RFID tags and drones to automate warehouse inventory management of perishables. Results from our analytical model and simulation analysis indicate that such warehouse automation is beneficial to both the warehouse operators and their customers.

IOT-Based Smart Door Selector For Double Security: Integration of Rfid and Blynk App for Economical Solution

This research aims to design and develop an Internet of Things (IoT)-based smart door lock system by utilizing Radio Frequency Identification (RFID) technology and the Blynk application. The system is designed to improve the level of home security by providing a variety of flexible access methods, including the use of RFID cards, smartphone apps, and emergency buttons. The prototype method is applied in system development, which involves creating a physical model to evaluate its performance and functionality. The test results showed that the system was operating properly, where RFID only responded to the registered card to unlock, thus increasing access security. Hardware design that is carried out with an economical approach and simple use of components makes this system more affordable compared to commercial smart door lock products on the market. This research shows great potential in increasing efficiency and comfort in managing home security systems and offers ease of implementation in various types of housing. Thus, this research makes a significant contribution to the development of innovative and practical solutions to improve home safety at a lower cost while providing a sense of security for its occupants.

DroneSilient (drone + resilient): an anti-drone system

AbstractIt is imperative to take a holistic strategy to thwarting drone threats, including the identification of drones and drone-like gadgets like ornithopters that visually imitate birds. In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. A modified bloom filter method is used to further identify the recognized object after a drone-like object has been found, allowing for the differentiation between regular drones, ornithopters, and genuine birds. The CNN (Convolutional Neural Network) method, created using the Google Cloud Platform and AutoML widget, is used in our model for object identification and categorization. DroneSilient has an RF sensor that can identify and imitate the threat presented by recognized drones. Convolutional Network, Modified Blooms Algorithm, RFID, and RF Sensor systems are all integrated into the DroneSilient system as part of this methodology combination, which provides a thorough method for identifying and eliminating drone threats. The bloom filter proposed takes 27.6 to 12.4 microseconds. Overall time for handling the unauthorized drone will be less than 180 s. By tackling every facet of the problem, our strategy outperforms many current anti-drone solutions in terms of functionality.

Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies

RFID (radio frequency identification) tags play a crucial role in a wide range of applications, from wireless communications to personal tracking and smart city infrastructure. These tags come in various shapes and sizes, prompting the authors to review the specialized literature and focus on optimizing planar designs with different geometries. This study prioritizes reducing the size of the most commonly used tags while enhancing their reliability. The primary objective of this article is to understand and improve the performance of planar RFID tags operating at 13.56 MHz through numerical simulations based on structures generated by algorithms developed in MATLAB. Building on previous research, the methodology is validated, followed by a detailed description of the algorithm designed and implemented by the authors in MATLAB to identify all possible structures that meet the design criteria. The authors compared various analyzed structures, considering different inductor shapes, dielectric materials, and thicknesses while examining their effects on gain and resonant frequency. The study also provides thermal analysis of the structures, and experimental validation of the studied designs. Finally, the researchers conclude with recommendations on the optimal structure for RFID tags.