RFID Research Articles

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.

Definition, detection, and tracking of nanowaste in foods: Challenges and perspectives.

Commercial applications of nanotechnology in the food industry are rapidly increasing. Accordingly, there is a simultaneous increase in the amount and diversity of nanowaste, which arise as byproducts in the production, use, disposal, or recycling processes of nanomaterials utilized in the food industry. The potential risks of this nanowaste to human health and the environment are alarming. It is of crucial significance to establish analytical methods and monitoring systems for nanowaste to ensure food safety. This review provides comprehensive information on nanowaste in foods as well as comparative material on existing and new analytical methods for the detection of nanowaste. The article is specifically focused on nanowaste in food systems. Moreover, the current techniques, challenges as well as potential use of new and progressive methods are underlined, further highlighting advances in technology, collaborative efforts, as well as future perspectives for effective nanowaste detection and tracking. Such detection and tracking of nanowaste are required in order to effectively manage this type ofwasted in foods. Although there are devices that utilize spectroscopy, spectrometry, microscopy/imaging, chromatography, separation/fractionation, light scattering, diffraction, optical, adsorption, diffusion, and centrifugation methods for this purpose, there are challenges to be overcome in relation to nanowaste as well as food matrix and method characteristics. New technologies such as radio-frequency identification, Internet of things, blockchain, data analytics, and machine learning are promising. However, the cooperation of international organizations, food sector, research, and political organizations is needed for effectively managing nanowaste. Future research efforts should be focused on addressing knowledge gaps and potential strategies for optimizing nanowaste detection and tracking processes.

Performance of a UHF RFID Detection System to Assess Activity Levels and Lying Behaviour in Fattening Bulls

Animal welfare strongly influences the health and performance of cattle and is an important factor for consumer acceptance. One parameter for the quantification of health status is the lying duration, which can be deployed for the early detection of possible production-related illnesses. Usually, 3D-accelerometers are the tool to detect lying duration in cattle, but the handling of bulls sometimes has special requirements because frequent manipulation in daily farming routines is often not possible. An ultrahigh-frequency (UHF) radio-frequency identification (RFID) system was installed in a beef cattle barn in Germany to measure the activity and lying time of bulls. Such UHF RFID systems are typically used for estrus detection in dairy cows via activity level, but can also be considered, for instance, as an early detection for lameness or other diseases. The aim of the study was to determine whether the estimations of activity level and lying duration can also be traced in husbandry systems for fattening bulls. Two groups of bulls (Uckermärker cattle, n = 10 and n = 13) of the same age were equipped with passive UHF RFID ear transponders. Three cameras were installed to proof the system and to observe the behaviour of the animals (standing, lying, and moving). Furthermore, accelerometers were attached to the hind legs of the bulls to validate their activity and lying durations measured by the RFID system in the recorded area. Over a period of 20 days, position (UHF RFID) and accelerometer data were recorded. Videos were recorded over a period of five days. The UHF RFID system showed an overall specificity of 95.9%, a sensitivity of 97.05%, and an accuracy of 98.45%. However, the comparison of the RFID and accelerometer data revealed residuals (ԑ) of median lying time (in minutes per day) for each group of ԑGroup1 = 51.78 min/d (p < 0.001), ԑGroup2 = −120.63 min/d (p < 0.001), and ԑGroup1+2 = −34.43 min/d (p < 0.001). In conclusion, UHF RFID systems can provide reliable activity and lying durations in 60 min intervals, but accelerometer data are more accurate.

Predator presence influences survival and behavior of translocated stream fish in ponds

Abstract Small artificial impoundments such as farm ponds have recently been recognized as potential habitat for threatened native fish species. However, factors influencing translocation or colonization success into these environments, including connectivity to stream networks and interactions with existing fish community, are largely unknown. In this study we conducted a controlled experiment to quantify the influence of piscivorous Largemouth Bass Micropterus salmoides on the survival of a translocated native minnow species that we used as a surrogate for federally endangered Topeka Shiner Notropis topeka. We translocated or released 100 Bluntnose Minnow Pimephales notatus into each replicate treatment ponds with and without Largemouth Bass in the summers of 2020 and 2021. Each minnow was implanted with a passive integrated transponder (PIT) tag. Translocated populations were monitored using stationary and mobile passive integrated transponder antennas and estimates of apparent survival and probability of detection for each pond were derived from open population mark-recapture models. Apparent survival was nearly two times higher in ponds without bass suggesting predation by bass leads to higher mortality. Additionally, probability of detection was nearly 10 times higher in ponds without bass, suggesting reduced movement of translocated minnows when bass were present. While the direct effect of mortality impacts translocated populations, the indirect effect of altered behavior may also be impactful on translocation success. These results confirm that Largemouth Bass can limit the success of translocated minnow species.

AtomGID: An Atomic Gesture Identifier for Qualitative Spatial Reasoning

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements.

Characterising a potential nearshore nursery ground for the blackchin guitarfish (Glaucostegus cemiculus) in Ma’agan Michael, Israel

The blackchin guitarfish Glaucostegus cemiculus has suffered severe declines and regional extirpation throughout its known distributions. While this species and its relative, the common guitarfish Rhinobatos rhinobatos, have been described in the Mediterranean Sea with co-occurring habitat ranges, no research has recorded the existence or extent of these two separate populations along the Israeli coastal waters. Along a particular coast in Israel, Ma’agan Michael, fishermen have reported annual observations of juvenile guitarfish between June to November for the last forty years. Based on these citizen-based observations the main research objective is to establish whether Ma’agan Michael fulfils all three criteria from the literature by Dr Michelle Heupel, allowing it to be acknowledged as a nursery ground for G. cemiculus. The methodology built for this objective integrates biological characteristics data with the identification of a recurrent seasonal distribution. Visual surveys exhibited a significantly higher abundance in Ma’agan Michael when compared to an adjacent area (Caesarea), with 2,096 recorded observations overall. Additionally, using a species-specific modified Catch and Release protocol, a total of 492 juveniles were captured with a beach seine net. During these capturing events, individuals were morphometrically measured and sampled for future genetic analyses. Out of these, 327 specimens were also fitted for PIT tags to track recaptures in subsequent captures. The highest abundance of neonates was caught from August to September each year (2017–2019), and all individuals captured during this study were identified in the field as G. cemiculus, ranging from 20–35 cm in length (85% of captures). Many specimens had an umbilical cord scar (n = 88), with a large percentage possessing visual remains of the yolk sac. For the first time, this study provides an inter-year description of the species Glaucostegus cemiculus present along the Israeli shoreline.

An Innovative Method Based on Wavelet Analysis for Chipless RFID Tag Detection

Chipless RFID tags have attractive low-cost advantages. However, traditional RFID anti-collision algorithms cannot be applied due to a lack of computing and processing capabilities. Problems with multitag detection must be solved to commercialize chipless RFID tags. In this paper, an innovative method for frequency-domain chipless RFID tag detection is proposed. The tags’ scattered signals are processed via wavelet analysis, and a time–frequency plot that can read the code is obtained. When the distance between tags is too close to distinguish in the time–frequency plot, independent component analysis is used to separate individual scattered signals from mixed echo signals; then, the code is read by means of wavelet analysis. To validate the proposed method, C-shaped frequency-domain chipless RFID tag models and a multitag detection simulation scenario were constructed in selected software. The short-time matrix pencil method (STMPM), short-time Fourier transform (STFT), and the proposed method were compared. When the tag spacing is 0.05 m, the code can be read successfully. Compared with the STMPM, the proposed method greatly reduces the computational quantity and shortens the reading time. Furthermore, adjustment of the window width and search step parameters is avoided.

Blockchain-enabled intelligent toll management system

Abstract Road toll tax contributes significantly in the economic development of any nation. In developing countries, the toll tax collection is carried out either manually or electronically. However, both approaches suffer from various challenges, including prolonged waiting times, lack of transparency, high operational costs, and concerns regarding data security and privacy. This research aims to address these challenges using a blockchain-based system. The proposed system employs advanced image processing techniques, specifically “You Only Look Once” version 5 (YOLOv5), to accurately capture and store vehicles’ registration numbers in a local server situated at toll plazas. Subsequently, the vehicle identification, along with the driver’s credentials, is transmitted to an application server, where an Ethereum smart contract verifies the information and automatically deducts the toll charges from the driver’s account. The results from this study indicate that the proposed system effectively reduces vehicle waiting time and facilitates uninterrupted vehicular movement. Additionally, the system ensures transaction transparency, safeguards the security and privacy of vehicle details, facilitates non-stop payments, rendering unnecessary cash payments or radio-frequency identification scanning at toll booths, and incorporates a decentralized architectural framework to enhance security and mitigate potential system failures.

An efficient key tag missing identification protocol for multiple regions in the Industrial Internet of Things

Radio Frequency Identification (RFID) is a special wireless network technology that uses radio frequency communication to achieve non-contact automatic identification through tags affixed to items. As one of the core technologies for physical layer communication in the Industrial Internet of Things(IIoT), RFID technology has been widely used in inventory management, supply chain management, access control identification, and many other areas. In practical application scenarios, the value and importance of some special tags far exceeds that of other tags. Therefore, in many cases, managers only need to pay attention to the status of these special tags, i.e., key tags, and not the status of all tags. However, the response of ordinary tags to reader commands will seriously interfere with identifying key missing tags. Therefore, we propose a novel protocol, Random Grouped Missing Key Tag Identification (RGKMTI), designed for multi-region key tag identification. RGKMTI employs a region-based sequential tag identification method to identify each group of tags individually, which effectively reduces collision slots and utilizes the duality property of slots containing only two key tags during the missing key tag verification phase, where the reader can verify two tags by transmitting at most a single tag ID, which significantly reduces the transmission load and shortens the total execution time. The experimental results show that the RGKMTI is significantly better than the existing schemes in terms of execution time and time efficiency, even though it reduces the identification time by 19.83% and improves the time efficiency by 6.82% compared with the best existing schemes.

A Hybrid Anti-Collision Protocol Based on Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA) for Radio Frequency Identification (RFID) Readers

Radio Frequency Identification (RFID) technology plays a crucial role in various Internet of Things (IoT) applications, necessitating the integration of RFID systems into dense networks. However, the presence of numerous readers leads to collisions, degrading communication between readers and tags and compromising system performance. To tackle this challenge, researchers have proposed Medium Access Control (MAC) layer protocols employing different channel access methods. In this paper, we present a novel solution, the Distributed Time Slot Anti-Collision protocol (DTS-AC), which employs a new TDMA notification system to address Reader-to-Reader Interference (RRI), while incorporating FDMA-based frequency resource management to resolve Reader-to-Tag Interference (RTI) collision issues. Simulation results demonstrate that DTS-AC significantly improves performance in dense RFID networks by enhancing read rates, with scalability benefits based on the number of readers, channels, and Time Slots (TSs). Moreover, the cost-effectiveness of DTS-AC facilitates efficient deployment in RFID networks, emphasizing considerations of time delay and data sensitivity.

Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si

Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si

Information Security based on IoT for e-Health Care Using RFID Technology and Steganography

The Internet of Things (IoT) allows you to connect a broad spectrum of smart devices through the Internet. Incorporating IoT sensors for remote health monitoring is a game-changer for the medical industry, especially in limited spaces. Environmental sensors can be installed in small rooms to monitor an individual's health. Through low-cost sensors, as the core of the IoT physical layer, the RF (Radio Frequency) identification technique is advanced enough to facilitate personal healthcare. Recently, RFID technology has been utilized in the healthcare sector to enhance accurate data collection through various software systems. Steganography is a method that makes user data more secure than it has ever been before. The necessity of upholding secrecy in the widely used healthcare system will be covered in this solution. Health monitoring sensors are a crucial tool for analyzing real-time data and developing the medical box, an innovative solution that provides patients with access to medical assistance. By monitoring patients remotely, healthcare professionals can provide prompt medical attention whenever needed while ensuring patients' privacy and personal information are protected.

Improved tag estimation method for TDMA anticollision protocols using CA-CFAR technique

Abstract Radio Frequency Identification (RFID) is considered a key element of the Internet of Things (IoT). It enables objects to be identified and tracked using radio waves. In RFID systems, several tags may be present within the range of the reader, leading to collisions and interference between signals that reduce system performance. Several anti-collision solutions have been proposed to solve this problem based on TDMA (Time-Division Multiple Access) technique, such as the ALOHA, Slotted ALOHA and Dynamic Framed Slotted ALOHA (DFSA) protocols. In this paper, we propose to improve TDMA anti-collision protocols based on the Cell Averaging Constant False Alarm Rate (CA-CFAR) scheme. The CA-CFAR technique is introduced to estimate the number of tags in the physical layer so that the reader can adjust its transmission parameters accordingly and avoid collisions. The obtained results show that this technique achieves good results in terms of system efficiency and the number of required slots to identify all the tags of the system.

Textronic Capacitive Sensor with an RFID Interface.

This article presents an innovative combination of textile electrical circuits with advanced capabilities of electronic RFID sensors, indicating the revolutionary nature of the development of textronics, which is used in various areas of life, from fashion to medicine. A review of the literature relating to the construction of textronic RFID identifiers and capacitive textronic sensors is performed. Various approaches to measuring capacity using RFID tags are discussed. This article focuses on presenting the concept of a capacitive sensor with an RFID interface, consisting of a microelectronic part and a textile part. The textile part is based on the WL4007 material, where antennas and capacitive sensors are embroidered using SPARKFUN DEV 11791 conductive thread. The antenna is a half-wave dipole designed to operate at a frequency of 860 MHZ. The microelectronic part is sewn to the textile part and consists of a microcontroller, an RFID-integrated circuit and a coupling loop, placed on the PCB. The embroidered antenna is coupled with a loop on the microelectronic module. This article focuses on presenting various designs of textronic electrodes, enabling various types of measurements. Article presents capacitance measurements of individual sensor electrodes, made using a measuring bridge and a built RFID tag. The sensors' capacity measurement results are shown.

Policy Approaches to Combat Livestock Theft and Enhance Security

Purpose: This study sought to analyze policy approaches to combat livestock theft and enhance security. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to policy approaches to combat livestock theft and enhance security. Preliminary empirical review revealed that a multi-faceted approach is essential to effectively address livestock theft. It emphasized the importance of integrating legislative measures, technological innovations, community-based initiatives, and enhanced law enforcement efforts. Technologies like GPS tracking and RFID tags were found effective but required financial support for widespread adoption. Community involvement and education were highlighted as critical for local surveillance and theft prevention. The study also underscored the need for robust legislative frameworks and international cooperation to deter theft and ensure consistent enforcement Unique Contribution to Theory, Practice and Policy: The Routine Activity Theory, Rational Choice Theory and Situational Crime Prevention Theory may be used to anchor future studies on policy approaches on combating livestock theft and enhance security. The study concluded that an integrated approach, including advanced security technologies, community involvement, and robust legislative frameworks, was essential for effectively reducing livestock theft. It highlighted the need for financial incentives to help farmers adopt GPS tracking and RFID tags, expanded community-based initiatives like Farm Watch, and stricter penalties for theft. The study also emphasized the importance of international cooperation for standardizing livestock identification practices and supporting cross-border law enforcement efforts. These recommendations aimed to enhance livestock security, mitigate financial losses, and improve community resilience Keywords: Livestock Theft, Livestock Security, Legislative Measures, Advanced Security Technologies, GPS Tracking, RFID Tags

Novel application of two- and four-lobed noise-canceling passive integrated transponder antennas for tracking fish in areas of high ambient electromagnetic interference.

Passive integrated transponder (PIT) technology is a leading tool for tracking fish in freshwater systems. PIT is highly applicable for assessing fish passage at anthropogenic infrastructure (e.g., dams and floodgates); however, there are often complications in operating PIT antennas near these structures due to the ambient electromagnetic interference of metal and power-supply equipment. We designed a PIT antenna that is resistant to the effects of ambient electromagnetic interference (AEMI). This design uses lobes with balanced polarity within the antenna to neutralize AEMI within the vicinity of the antenna. This novel PIT antenna provides a more effective and cost-efficient option for researchers tracking fish in environments with high AEMI.

Empowering the Blind: Contactless Activity Recognition with Commodity Software-Defined Radio and Ultra-High-Frequency Radio Frequency Identification.

This study presents a novel computational radio frequency identification (RFID) system designed specifically for assisting blind individuals, utilising software-defined radio (SDR) with coherent detection. The system employs battery-less ultra-high-frequency (UHF) tag arrays in Gen2 RFID systems, enhancing the transmission of sensed information beyond standard identification bits. Our method uses an SDR reader to efficiently manage multiple tags with Gen2 preambles implemented on a single transceiver card. The results highlight the system's real-time capability to detect movements and direction of walking within a four-meter range, indicating significant advances in contactless activity monitoring. This system not only handles the complexities of multiple tag scenarios but also delineates the influence of system parameters on RFID operational efficiency. This study contributes to assistive technology, provides a platform for future advancements aimed at addressing contemporary limitations in pseudo-localisation, and offers a practical, affordable assistance system for blind individuals.

Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling

Unintentional electromagnetic (EM) emissions often include information about the data processed by electronic devices. Intrusion based on an unintentional EM emission leaves no evidence of an attacker’s activity, while the data owner is unaware that it has been lost. EM attacks can be performed without physically damaging a device that operates regularly. The most typical intrusion activities involve sensitive data exfiltration using various methods that do not require the physical connection of devices to the computer network or communication channels. This research examines EM emissions from computer monitors, wireless keyboards and mice, printers, scanners, conductors, piezoelectric sensors (PES), and radio frequency identification (RFID) devices. The telecommunication electronics material protected from emanating spurious transmissions (TEMPEST) profiling as a performance engineering of the EM footprint is discussed. This study also presents different TEMPEST standards and highlights their importance concerning unintentional EM radiation.

ROI Analysis of RFID-Based Intelligent Inventory Management Systems

This paper investigates the return on investment (ROI) of RFID-based intelligent inventory management systems. RFID (Radio Frequency Identification) technology has revolutionized inventory management by providing real-time visibility, improved accuracy, and enhanced operational efficiency. The study employs a mixed-methods approach, integrating both qualitative insights from industry experts and quantitative data from surveys and existing literature. Initial setup costs, ongoing maintenance expenses, and tangible and intangible benefits are thoroughly analyzed. The findings reveal that while the initial investment in RFID technology can be substantial, the benefits in terms of reduced labor costs, minimized inventory shrinkage, and improved inventory accuracy significantly outweigh the costs. ROI calculations, including payback period and net present value (NPV), demonstrate the economic viability of RFID systems. Comparative analysis shows that RFID systems outperform traditional inventory management systems, especially in sectors such as retail and healthcare. This paper provides a comprehensive framework for evaluating the ROI of RFID technology and offers practical recommendations for businesses considering its adoption.

Development of IOT-based Smart Anti – Theft Latch

In our day to day lives, security is of utmost concern. Everyone wants to be as secured as possible. This paper develops an Internet of Things (IoT)-based smart security system and supports the concept of a "IOT close to life and easy to use”. One of the many useful applications of IoT is home security; among other things, we're leveraging it to build affordable home security systems. The IoT is a network of physically connected objects that can exchange data and communicate with one another without the need for human intervention. The simplicity of setup, low cost, and minimal maintenance are some of this system's key benefits. This system is executed with the help of Raspberry Pi 3B. Face recognition system used determines if the individual is lively enough to receive authorization. If an unauthorized access is detected then the buzzer will be triggered and an alert notification will be sent on an app which is designed for the owner which will also include a photo detected by the camera and with an option to reset the buzzer. This system comprises of dual authentication that is Radio Frequency Identification (RFID) key and a face recognition system. This approach is able to protect the homes from intrusions by continuous detection of the suspicious actions using soft computing, that is, Convolutional Neural Network (CNN) algorithm for face recognition.