RFID System Research Articles

Research on Comparison of Security Protocols for RFID Systems

Abstract: As the adoption of Radio Frequency Identification (RFID) technology continues to expand, concerns about its security have grown in significance. And the study of RFID security protocols aims to enhance system security and to provide solutions that can be applied to a variety of scenarios. Therefore, this paper delves into the security protocols employed in RFID technology, investigating their applications across a range of domains such as logistics tracking, financial payments, and access control systems. Besides, through a combination of literature review and comparative analysis, the study provides an in-depth exploration of the working principles, strengths, weaknesses, and suitable use cases of protocols based on cryptography, challenge-response, and time-based mechanisms. The results indicate that cryptography-based protocols perform well in high-security environments such as financial payments, albeit with high computational complexity. Challenge-response-based protocols enhance authentication in access control systems, while time-based protocols successfully mitigate replay attacks through timestamping mechanisms, making them ideal for intelligent transportation systems. In short, each security protocol presents distinctive strengths, and the selection or integration of these protocols should be tailored to specific application scenarios to create more secure and reliable RFID systems.

Advanced Security Strategies for Software Vulnerability Protection: A Multi-Scale Detection Model With FPGA-RFID

This paper proposes a multi-scale software vulnerability detection model using Radio Frequency Identification (RFID) target recognition technology and Field-Programmable Gate Array (FPGA) hardware design. The systematic analysis of RFID systems and FPGA architectures provides the foundation for constructing robust security solutions against code-based threats. The Scalable Vulnerability Detection Method (SVDM) is developed leveraging multi-scale code metrics and deep feature extraction techniques. The evaluations of vulnerability datasets CWE-119 and CWE-399 demonstrated over 84% accuracy, recall, and F1 score. The precision, recall, and F1 score of the CWE-399 vulnerability dataset in the SVDM were 85.15%, 85.12%, and 84.79%, respectively, while the values of the CWE-199 dataset were 83.12%, 82.16%, and 82.38%, respectively. Compared with existing methods, the functionality of SVDM was further highlighted. This research provides an extensible and adaptable framework for identifying security vulnerabilities with both hardware and software techniques. It can optimize threat detection, risk analysis, and system integrity across software development and operation life cycles, providing technical reference and scientific support for the security protection of software vulnerabilities, while promoting the development of industries such as intelligent monitoring and traceability.

Techno economics and energy dynamics of a solar powered smart charging infrastructure for electric vehicles with advanced IoT based monitoring and RFID based security

The widespread adoption of electrical vehicles (EVs) has led to an increased demand for charging stations which required an adequate charging infrastructure. However, this challenge brings substantial financial and ecological implications for the power grid and other major stakeholders such as EV users, system operators and policymakers. To resolve this issue, this work proposes a smart EV charging infrastructure that provides 1) effective load management of EVs, 2) real-time monitoring of charging slots at a charging station, 3) smart security system, and 4) integration of renewable energy resources (RERs) into charging stations. This is achieved by setting out how to incorporate photovoltaic (PV) system into charging station infrastructure while suggesting effective EV load management with optimal energy utilization and minimal energy costs. Also, the annual power production of PV systems and power sharing between different power resources has been investigated to enable effective decisions about EV scheduling. The proposed infrastructure also offers an internet-of-things (IoT)-based monitoring of fast/slow charging units with real-time slot tracking to the users and charging station operators. The security aspect of the charging station is covered by a sensor-driven radio frequency identification (RFID) system. Experimental results that the proposed system generates 1,055,326 kWh/year, reducing grid reliance by 50% (to 335,775 kWh) and lowering CO2 emissions from 425,326 kg to 212,210 kg. Moreover, energy costs drop from $0.30 to $0.13 per kWh and 270,567 kWh/year energy is sold back to the grid. The proposed system yields a net present revenue of $1,020,000 which demonstrates significant economic and environmental benefits.

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.

An Ultra High Frequency Radio Frequency Identification Compatible Circular Polarized Microstrip Antenna Array

This study focuses on the development, simulation, and practical validation of a metal-only microstrip patch antenna and its array designed to increase gain. The antenna is specifically designed to meet the growing demand for reliable and efficient RFID systems in various fields, such as asset tracking, inventory management, and smart logistics. Our design utilizes a truncated patch with an air substrate to achieve high gain and circular polarization. The antenna's dimensions measure 468 × 188 × 31 mm³ and deliver impressive performance metrics, boasting a return loss |S11| of -18.21 dB and an estimated gain exceeding 10.6 dBi. These figures compare favorably with the simulated results, which indicate an |S11| of -20.8 dB and a total gain of 11.2 dBi. Our microstrip antenna array demonstrated consistent Circular Polarization quality throughout the radiation angle, with an Axial Ratio of less than 3 dB. This antenna has emerged as a compelling solution for UHF-band RFID technology to meet the demands of various real-world applications.

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.

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.

Analisis dan Perancangan Sistem Smart Parking Berbasis RFID dengan Penggunaan Kartu Tanda Mahasiswa (KTM) untuk Mengurangi Risiko Pencurian Kendaraan

Parking issues at Universitas Prof. Dr. Hazairin, SH (UNIHAZ) highlight the inefficiency and insecurity of the current manual system. This study proposes an RFID-based smart parking system integrated with IoT technology to enhance security and efficiency. Utilizing the ESP32 microcontroller with Wi-Fi and Bluetooth, the system enables real-time data communication and control of parking gates. Students can access parking by tapping their RFID-enabled ID cards (KTM), triggering gate access if registered. Security is bolstered by cameras capturing vehicle images. The RFID system offers high reading speed and durable tags, aiming to mitigate theft risks and improve user convenience. This research focuses on designing a safer, more efficient parking solution for UNIHAZ

Design of Provably Secure and Lightweight Authentication Protocol for Unmanned Aerial Vehicle systems

Drones also called Unmanned Aerial Vehicles (UAVs) have become more prominent in several applications such as package delivery, real-time object detection, tracking, traffic monitoring, security surveillance systems, and many others. As a key member of IoT, the group of Radio Frequency IDentification (RFID) technologies is referred to as Automatic Identification and Data Capturing (AIDC). In particular, RFID technology is becoming a contactless and wireless technique used to automatically identify and track the tagged objects via radio frequency signals. It also has drawn a lot of attention among researchers, scientists, industries, and practitioners due to its broad range of real-world applications in various fields. However, RFID systems face two key concerns related to security and privacy, where an adversary performs eavesdropping, tampering, modification, and even interception of the secret information of the RFID tags, which may cause forgery and privacy problems. In contrast to security and privacy, RFID tags have very limited computational power capability. To deal with these issues, this paper puts forward an RFID-based Lightweight and Provably Secure Authentication Protocol (LPSAP) for Unmanned Aerial Vehicle Systems. The proposed protocol uses secure Physically Unclonable Functions (PUFs), Elliptic-Curve Cryptography (ECC), secure one-way hash, bitwise XOR, and concatenation operations. We use Ouafi and Phan’s formal security model for analyzing security and privacy features such as traceability and mutual authentication. The rigorous informal analysis is carried out which ensures that our proposed protocol achieves various security and privacy features as well as resists various known security attacks. The performance analysis demonstrates that our proposed protocol outperforms other existing protocols. In addition, Scyther and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool simulation results demonstrates that there is no security attack possible within bounds. Therefore, our proposed LPSAP protocol achieves an acceptable high level of security with the least computational, communication, and storage costs on passive RFID tags.

Development of A Radio Frequency Identification System for The Manufacturing Packing Process Via The DMAIC Approach

Quality has long been a critical metric for determining a company's competitiveness in the business world. The use of tools, methods, and concepts to improve and regulate product quality has been widespread. The purpose of this research is to solve the missing accessories issue that occurs during the packing process by using the Lean Six Sigma tool DMAIC (Define, Measure, Analyze, Improve, and Control). As part of the Lean Six Sigma quality effort, the DMAIC technique is frequently defined as a problem-solving methodology and a data-driven quality strategy. The actual root cause for these missing accessories is the lack of a proper validation process during the packing process. A brainstorming session was held to develop solutions to this problem, and one suggestion was to employ the radio frequency identification (RFID) validation system. This new implementation was set up during the kitting process by pasting RFID labels for all accessories. These accessories were validated back during the packing process to ensure no missing accessories after the operator sealed the carton box. After implementing the RFID system, the company managed to reduce the cost of manpower and parts that needed to be replaced in case of missing data. It also improves the efficiency of the business, as the addition of a validation RFID system avoids mistakes by detecting missing accessories before the product is delivered to the customer.

Face Recognition-Based Door Lock Security System Using TensorFlow Lite

A door security system utilizing face recognition technology based on TensorFlow Lite has been developed to enhance access security and convenience. This research aims to design a system capable of accurately recognizing faces in real time, integrating it with door lock devices, and ensuring user data security. Employing the waterfall method, the system was implemented using an ESP32-CAM microcontroller and deep learning algorithms. Testing results demonstrated a face recognition accuracy of 91% in identifying and processing commands from 200 trials with ten facial variations. Successful integration with door lock devices was achieved through serial communication. The system also features activity log recording for monitoring purposes. This solution offers greater practicality and security than RFID systems as it eliminates the need for physical cards. This research contributes to developing more sophisticated and user-friendly home security systems, with the potential for further enhancements in recognition capabilities under various lighting conditions and integration with other biometric technologies

Transmission design for ISAC‐WPT backscatter RFID systems

AbstractIn this letter, a novel integrated sensing and communication (ISAC) scenario with wireless power transfer (ISAC‐WPT) is investigated. In this new setup, an access point simultaneously employs communication and sensing beams to serve a communication user, detect an RFID tag, and power an energy receiver. The objective is to design the sensing and communication beams to minimize the total transmit power while satisfying the requirements of tag detection, communication, and energy harvesting. To address the corresponding non‐convex problem, the constraints are reformulated into convex second‐order cone constraints, thereby obtaining the globally optimal solution. To further reduce computational complexity, zero‐forcing is employed to design the beamforming vectors, followed by solving a convex optimization problem to determine the optimal power allocation between sensing and communication beams. The simulation results demonstrate the effectiveness of the proposed beamforming designs, highlighting the potential of ISAC‐WPT Backscatter RFID systems.

Multilevel Printed Wearable Radio-Frequency Intelligent Identification Platform for Object Recognition.

As a noncontact target recognition technique, radio-frequency identification (RFID) technology demonstrates attractive potential in constructing human-machine interaction (HMI) systems. However, the current development of RFID technologies in HMI systems is hampered by critical challenges in manufacturing high-performance RFID readers with superior flexibility and wearing comfort. Hence, we propose a multilevel printing strategy to overcome the difficulties in manufacturing high-performance large-scale microwave systems. Compared to traditional processes, the RFID system fabricated by the hybrid additive manufacturing technique exhibits equivalent electromagnetic performance and has obvious advantages in terms of manufacturing cost and environmental friendliness. A printed reconfigurable antenna with intelligent radiation mode is seamlessly integrated with the reader circuit via a "one-step" printing technology. Additionally, through chemical doping and artificial intelligence (AI) prediction, we have developed a modified polydimethylsiloxane (PDMS) encapsulation to miniaturize the system volume and enhance reliability. Electromagnetic and mechanical measurements demonstrated that our flexible RFID platform offers superior reliability and stability during long-term daily use. The RFID platform possesses exceptional capabilities in target positioning and accurate identification, demonstrating unique potential in noncontact sensing and recognition, which are highly demanded by flexible and wearable HMI systems.

Rfid Technology in Library and Information Science

The radiofrequency identification system (RFID) makes it possible for devices like computers or machines to automatically recognize things, record metadata, or control certain targets. A technique called Radio Frequency Identification (RFID) makes it possible to identify objects without a direct line of sight. For administering the automated library and detecting theft, libraries are using the most recent technologies. The inventory, issuance, and return status of books, gadgets, and other objects in the library can all be monitored with the help of RFID systems. Additionally, it ensures that your library is secure and that no priceless resources are stolen from it. The library management system receives the ID number from the RFID system and uses it to retrieve the book, check it out, and produce a receipt. When a visitor returns a book, the tag responds with the ID number, and the system credits the user's account. The automated identification of library documents involved in a certain technical process requires reading data from the tag memory located in the working region of the RFID reader. Radio-frequency labels of the same type as the library labels but not those can also fall within the reading zone at the same time. Libraries must make sure that their RFID systems adhere to all applicable laws, keep them updated often, and instruct their employees and visitors on how to use them effectively.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

An Effective Tag Estimation Method Based upon Artificial Neural Networks and Signal Strength for Anticollision in Radio Frequency Identification Systems

Radio frequency identification (RFID) technology has been widely used in applications such as access control, inventory management, spatial positioning, and object identification. Accurate tag estimation is one of the major challenges in RFID reader systems particularly in areas where large tag populations are to be identified such as shopping carts, warehouse inventory monitoring, and small ruminant farms. This paper proposes a new tag estimation technique employing artificial neural networks (ANNs) and signal strength to read large tag populations. The technique estimates the number of tags through the signal strength of the backscatter channel for efficient implementation of dynamic framed slotted Aloha (DFSA) protocol by analyzing the RN16 and the received signal strength indicator (RSSI). The ANN model is trained using the signal strength of various tag populations and can identify the number of tags with minimal errors. The proposed technique does not require any modification in the tags and is implemented as a minimal software script to be added to the tag estimation module of the reader. The proposed signal strength-ANN model is able to estimate the accurate number of tags thereby improving the performance of the employed DFSA model.

The environmental benefits and burdens of RFID systems in Li-ion battery supply chains – An ex-ante LCA approach

This article explores the current and future technological pathways of RFID components and quantifies their environmental impacts through life cycle assessment (LCA), and also covering the critical materials and energy use of tags, readers, and backend servers. We assess RFID-enabled lithium-ion battery supply chains with different cathode chemistries – lithium nickel cobalt aluminum oxide (NCA), nickel cobalt manganese oxide (NCM) as case applications. The results indicate that miniaturization and novel antenna materials can reduce the current environmental impacts of tags and readers by 60 % and 4 %, respectively. Direct energy consumption and antennas are the main contributors for tags, while microcontrollers and transceivers are the main contributors for readers. Furthermore, the overall environmental improvements of RFID system outweigh their additional environmental impacts. The use of RFID systems in optimizing battery supply process gives higher reductions in climate change and abiotic resource depletion impacts than switching to low-carbon battery chemistries (i.e. using NCM instead of NCA).

DNA TRIKKY Based Security Mechanism for Radio Frequency Identification Protocol

Radio frequency identification (RFID) technology has seen rapid expansion in recent years, used in a variety of applications like tracking, monitoring, ticketing, supply-chain management, contactless payments, etc. Several researchers have recommended elliptical curve cryptography (ECC) as a great cryptosystem to pair with RFID technology to increase security and privacy. However, RFID systems have major problems with insecure communication routes, which pose security concerns. In this research, a DNA TRIKKY based security mechanism has been proposed for RFID protocol to mutually authenticate the RFID tag and reader through insecure channels. The proposed solution uses a sponge structure and DNA-XOR to enhance the hash function generation process, preventing sensitive data from being leaked from the backend server. Informal security evaluations provide proof that the proposed method can withstand many attacks. According to performance evaluation, the proposed method performs better than the current protocols, making it ideal for use with RFID systems since it necessitates less processing time, communication costs, and storage costs particularly for the tag. According to the results, the proposed authentication paradigm works well with RFID tags that have limited resources. A comparison of the proposed protocol to other protocols shows that it performs more securely. Studies have found that this method outperforms other existing strategies.

RFID-GSM Automatic Gate Control for Highly Secure Organizations

Abstract: In the current dynamic global landscape, security is paramount for organizations, particularly those entrusted with safeguarding sensitive data and valuable assets. Automatic gate control systems are pivotal in fortifying security and optimizing the operational efficiency of access points for highly secure organizations. By integrating Radio-Frequency Identification (RFID) and Global System for Mobile Communications (GSM) technologies, these systems provide sophisticated features that bolster security measures while enabling seamless access for authorized personnel. These automatic gate control systems provide a multi-layered approach to security, addressing various vulnerabilities and potential threats highly secure organizations face. By leveraging RFID technology, these systems enable efficient and reliable authentication of authorized personnel, minimizing the risk of unauthorized access or breaches. Additionally, the integration of GSM technology enhances the system's responsiveness and remote management capabilities, allowing administrators to monitor gate activities in real time and respond promptly to any security incidents or anomalies.

Automated monitoring of brush use in dairy cattle.

Access to brushes allows for natural scratching behaviors in cattle, especially in confined indoor settings. Cattle are motivated to use brushes, but brush use varies with multiple factors including social hierarchy and health. Brush use might serve an indicator of cow health or welfare, but practical application of these measures requires accurate and automated monitoring tools. This study describes a machine learning approach to monitor brush use by dairy cattle. We aimed to capture the daily brush use by integrating data on the rotation of a mechanical brush with data on cow identify derived from either 1) low-frequency radio frequency identification or 2) a computer vision system using fiducial markers. We found that the computer vision system outperformed the RFID system in accuracy, and that the machine learning algorithms enhanced the precision of the brush use estimates. This study presents the first description of a fiducial marker-based computer vision system for monitoring individual cattle behavior in a group setting; this approach could be applied to develop automated measures of other behaviors with the potential to better assess welfare and improve the care for farm animals.