This paper contributes a novel RFID-FPGA architecture for vehicle detection and speed measurement systems providing a theoretical framework and experimental evidence of the optimization of read zones of RFID antennas employed in traffic control. Contrary to other methodologies in which the positioning of the RFID antenna is taken empirically, the novelty of this study consists in determining the optimal read zone of the antenna by mathematical modeling employing the Friis transmission equation. This model will then be implemented in FPGA technology employing HDL programming language, thus allowing deterministic and reprogrammable estimation of the velocity of the vehicle. Experimental data obtained for motorbikes provide measurement accuracy of ±1 km/h with time resolution of 10 ms. Some of the novelties introduced in this work include the fact that the distance between the RFID antenna and the vehicle determines the maximum speed of the latter that can be detected. Moreover, the proposed FPGA-based architecture allows dynamic reconfiguration according to changing traffic conditions.
This study presents a comprehensive bibliometric analysis of research on Radio Frequency Identification (RFID) technology, pursuing two main objectives: (i) mapping the intellectual structure and thematic evolution of the field, and (ii) modelling the factors that influence the citation impact of publications. The bibliographic dataset consists of 47,357 documents extracted from Scopus and published between 1999 and 2023. The methodology combines descriptive bibliometric analyses (publication trends, authorship patterns, and keyword analysis) with a multi-group Structural Equation Model examining the role of various publication characteristics in determining the annual citation rate of a paper, taking into account different document types (journal articles, review papers, conference documents, and books/book chapters). The keyword analysis reveals a highly skewed distribution across four thematic categories, with a small core of well-established themes (Internet of Things, security, supply chain management, authentication, localization) dominating both frequency and persistence rankings, alongside a larger set of trendy topics (blockchain, machine learning, Industry 4.0) that have generated significant citation peaks despite their limited temporal persistence. The results of the Structural Equation Model indicate that publication age is the most structurally dominant predictor of citation accumulation, consistent with cumulative advantage dynamics, while the effect of title characteristics varies across document types. Citation impact is highly concentrated in a small subset of publications, a pattern that persists even when the analysis is restricted to journal documents. The findings offer practical guidance for researchers seeking to maximize their scientific impact in the RFID field.
Counterfeiting is a pervasive issue in global markets, significantly impacting various industries and supply chains, particularly luxury fashion. The expansion of the “global bazaar economy” has facilitated the rise of counterfeit goods, driven by consumer demand for economically affordable yet trendy products. This study explores the state-of-the-art of anti-counterfeiting technologies in luxury fashion by means of a literature review carried out on 75 documents retrieved from Scopus database. Both bibliometric and content-related analyses were performed on the sample, using Excel and VOSviewer software. Among the most common technological solutions, RFID stands out, followed by holographic labels, barcodes and watermarks; a Strength–Weaknesses–Opportunities–Threats (SWOT) analysis is provided for each of these most spread technologies, which corresponds to the originality of this research. Moreover, starting from 2018, blockchain turned out to be relevant, so anti-counterfeiting technologies should enable this issue. Another relevant finding revealed that consumer cares about being able to assess himself the originality of products, so innovative solutions should increase consumer experience in this sense.
This paper presents the design, integration, and experimental validation of a novel lightweight RFID reader specifically developed for Unmanned Aerial Vehicles (UAVs). The system was engineered to meet the strict constraints of aerial platforms, including weight, power consumption, and communication reliability. The resulting 1 kg reader, integrated with a DJI Matrice 600 drone, maintained full flight endurance and demonstrated robust performance at altitudes up to 10 meters. A series of 50 experimental flyovers in a warehouse-style corridor confirmed that tag orientation significantly affects read success: top-mounted tags achieved nearly 30 reads per pass, while side-mounted and partially covered tags showed reductions of approximately 50% and 65%, respectively. These findings highlight the critical role of tag visibility and provide practical guidance for flight-path planning and tag placement in logistics environments. Unlike previous studies that focused on isolated technical aspects or specific applications, this work offers a comprehensive evaluation of UAV-based RFID systems, combining hardware innovation, rigorous performance testing, and broad applicability. The results confirm the feasibility of aerial RFID inventory and lay the groundwork for scalable, autonomous asset tracking in complex industrial settings.
In the textile industry, labeling errors during the production process present significant challenges for both quality control and product traceability. This study proposes a real-time RFID-based production tracking and label quality control system to address these issues. In the developed system, RFID tags attached to products are read by embedded devices equipped with RFID readers integrated into the production lines. The captured product code data is transmitted to a local server via MQTT. The server verifies the tags by querying a central database and provides instant feedback to the corresponding device. The system runs entirely over a local network and does not require an external internet connection, ensuring uninterrupted functionality even in infrastructure-limited environments. This architecture enables all devices on the production line to communicate synchronously with the server, maintaining system-wide consistency and integrity. In this way, the use of a single tag per product is ensured, and faulty tags are filtered out. Furthermore, the system evaluates tag readability to detect quality issues such as stitching errors, physical deformation and alerts the operator through feedback. As a result, defective products are identified and removed before production is completed, helping to reduce time loss, and improve overall production reliability.
The paper offers an extensive review of literature regarding the role of RFID technology in promoting the circular economy (CE). Initially, it underscores the urgent need for resource recirculation to minimize waste and prevent the depletion of natural resources. Subsequently, it explores how RFID can improve traceability and enhance the efficiency of supply chains. The paper also delves into various CE frameworks, particularly focusing on the 9R framework, which encompasses strategies like Reduce, Reuse, and Recycle, among others. The literature is classified by industry sector, study type, and CE principles that RFID could enable or facilitate. Additionally, the paper highlights the significance of digital technologies in facilitating the transition to CE and examines whether RFID could be considered a key CE enabler within the IoT infrastructure. The findings confirm that RFID has the potential to support a range of CE strategies across various industry sectors, emphasizing sustainability and efficient resource use. The paper concludes by synthesizing RFID’s contributions to the CE transition thus far and identifies future research areas, such as exploring approaches that incorporate other digital technologies alongside RFID to enhance and accelerate the CE transition.
Traceability and visibility of outbound logistics are crucial for companies aiming to enhance customer satisfaction and ensure product quality and reliability. The Internet of Things (IoT) offers promising solutions by enabling real-time tracking and intelligent decision-making in supply chains. However, processing and interpreting heterogeneous IoT data (sensors, actuators) remain challenging, as timely and accurate information dissemination is required. In this paper, we propose EDSOA-OLP-IoT; novel semantic middleware architecture based on the OLP-IoT ontology and designed to optimize outbound logistics operations. Our approach integrates a service-oriented event-driven architecture with a Publish-Subscribe communication model, complex event processing (CEP), and ontology-based reasoning. Unlike traditional IoT frameworks, our system enhances anomaly detection, improves decision-making accuracy, and optimizes resource management by leveraging semantic reasoning. Through experimental simulations, we demonstrate that EDSOA-OLP-IoT effectively reduces response time to critical events and enhances supply chain efficiency. To validate our approach, we conducted simulations based on real-world-inspired scenarios, including temperature monitoring in refrigerated trucks and warehouses. These scenarios showcase the system's ability to detect anomalies and trigger appropriate responses, highlighting the potential of semantic reasoning and event-driven architectures for real-time logistics optimization.
Inventory management is central to efficient management systems for increased productivity and tracking of resources. Industry 4.0 (I4.0) revolutionises the manufacturing sector by integrating advanced digital technologies like the Internet of Things (IoT), leading to smart factories with enhanced efficiency and flexibility. Inventory management compliments this transformation, particularly if it includes tracking technology and Visual Management Systems (VMS), which convert tracking data into actionable insights, optimising processes and reducing waste. This paper presents a novel application of Radio Frequency Identification (RFID) tracking technology in conjunction with a Business Intelligence (BI) VMS for tracking inventory trolleys within a large-scale aerospace manufacturing environment. An RFID-VMS system including passive RFID cards, a USB hub for remote connection and a Power BI VMS were implemented providing intuitive key performance indicator (KPI) visuals, mobile accessibility and increased production line efficiency by 45%. Missed takt times decreased by 52.2% and line stops decreased by 75% indicating substantial improvements in maintaining the production schedule and reducing interruptions. The prototype implementation cost $600 covering RFID hardware, software, and infrastructure updates. Scaling to a full production line would require an estimated investment of $36,000 with a projected annual waste cost reduction of approximately $2.45 million. The number of blue cards increased by 7.9% reflecting an increase in throughput and production efficiency. As a result, the system supports sustainability by reducing operational waste and optimising inventory management, aligning with UN Sustainable Development Goals (9: Industry, Innovation, and Infrastructure and 12: Responsible Consumption and Production) and promoting economic growth. This simple, yet effective system framework could be applied to a number of sectors where materials must be tracked and recorded into and out of different locations at different intervals.
While the economic and environmental impacts of Radio Frequency Identification (RFID) deployments have been extensively studied, their effects on employee working conditions, remain largely unexplored in the literature. This paper addresses this gap by proposing a methodology to quantify the social sustainability of RFID deployments. The methodology employs two key indicators derived from a questionnaire administered to employees utilizing RFID technology in their daily tasks. The first indicator, adapted from the Net Promoter Score (NPS), measures employees' propensity to recommend RFID implementation. The second is an analytic index, based on a modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which assesses the impact of RFID systems on four pillars: empowerment, enrichment, engagement, and manage & control. A case study of a fashion retail company that implemented RFID technology across its retail stores and headquarters validates the methodology. Results demonstrate that RFID deployment led to significant improvements in all four dimensions, notably increasing employee satisfaction, collaboration, support, commitment and productivity. Indeed, results show that employees who are using RFID in their daily work would recommend the adoption to friends and colleagues who do not. This general-purpose framework can be applied to any organization implementing RFID technology, providing a valuable and extensible tool for assessing the 'People Return on Investment' (PROI).
Comprehensive occupancy information in smart buildings has become more imperative in order to develop new control strategies in energy management systems. Several techniques can be used to collect occupancy information considering accurate sensing techniques, such as passive infrared (PIR), carbon dioxide (CO2) and different types of cameras (i.e., thermal, or optical cameras). Recent studies show the usefulness of integrating occupancy information into energy management systems to reduce energy consumption while maintaining the occupants’ comfort. The purpose of this work is to elaborate a comprehensive review on occupancy detection systems in smart buildings. This study presents a set of comparison standards including methods, occupancy resolution, type of buildings and sensors. A classification of different approaches, which can be implemented and integrated into the building management system for detecting indoor occupancy, is introduced. Summary and discussions are given by highlighting the usefulness of machine learning for enabling predictive control of active systems in smart buildings.
This paper proposes an intelligent warehouse-picking approach using radio frequency identification (RFID) indoor positioning and natural language processing (NLP) speech recognition. A forward maximum matching algorithm segments speech into domain terminology. Location was estimated by RFID signal strengths between reference tags and pickers. Simulation results demonstrated a 50% reduction in segmentation runtime versus conventional methods. Speech recognition accuracy reached 90–95%, improving by 23% over baseline. Positioning accuracy also increased substantially. The techniques can reduce picking errors and costs. Further work should evaluate performance in real-world environments.
This document focuses on the Ultra-Wideband Modified Circular Robo structure Antenna (UWCRSA) with partial ground for the sub-7 GHz band (4.17 GHz to 7 GHz). The overall geometry of the proposed antenna is 12×18×0.8 mm3. The designed structure is obtained by placing a simple modified circular robotic structure patch with dielectric constant ( ɛ r) and dissipation factor (tan δ) of 4.4 and 0.02, respectively, on the FR4 substrate. In this document, we examine the design technique, complexity, overall geometric parameters, substrate used, feed technique and performance analysis of the proposed antenna. The semi-grounded radiating patch design is primarily suitable for cellular vehicle applications and anything below 7 GHz (C – V2X) (IEEE 802.11p standard) and WIFI enhanced 3rd version (IEEE 802.11ac standard). Simulation results for the proposed antenna were generated with a Radio Frequency Structure Simulator (HFSS) and measurements were obtained with a Rohde and Schwarz ZNB 20 network analyzer. Simulation and measurement results are measured in terms of S11, VSWR, bandwidth, gain, directivity, efficiency and radiation pattern. Antenna results show good agreement and can be used for C – V2X, WIFI Extended 3rd Version and satellite communications.
Real-time monitoring and fault diagnosis of transformers are essential for the stable power system operation. This paper presents an RFID-based transformer fault feature extraction and classification algorithm. Experiments show that monitored current signals are stable while the temperature peak is 356°C. Hilbert decomposition reveals regular current and voltage patterns that can be used as fault indicators. Signal strength classification accuracy reached 80% . At rated load, the transformer temperature soared to 186°C, indicating overheating issues. The monitoring during a sample day showed that overload events were concentrated from 16:00-20:00, which required attention. The approach helps accurately identify transformer fault types from real-time RFID data for proactive maintenance. Compared to reactive repairs after failures, this not only improves employee productivity but also reduces costs. Based on customized RFID deployment, the algorithm contributes to the stability and economy of power infrastructure.
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.
Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications.
In recent years, an innovative passive Bluetooth Low Energy (BLE) technology, namely Wiliot technology, has emerged as an alternative to passive UHF RFID. This paper proposes an experimental campaign aimed at measuring and evaluating the performance of Wiliot technology, when applied to typical use cases of passive UHF RFID technology. In particular, 9 laboratory tests were conducted to assess read distances, temperature tests, inventory accuracy, and tracking. The results provide interesting feedback, especially regarding reading distances, inventory accuracy, and temperature, suggesting that Wiliot technology could be successfully utilized in these use cases. The tracking tests, however, revealed some limitations, primarily due to the requirement for the tag to remain stationary in front of the bridge for at least 10-15 seconds to obtain consistent results. Although the number of tests conducted is limited, they represent a first experience with the targeted technology, and the results can be useful to both researchers and practitioners. The former can find suggestions for further tests to enhance the technology and its potential use in real operating environments, while the latter can derive suggestions for possible practical implementations.
The 2019 coronavirus pandemic is considered a global health emergency and the greatest challenge human kind has faced since World War II. The disease has claimed many lives globally and has caused major economic deterioration. The most challenging is the constant change of the virus through mutations leading to emergence of different variants. There are stipulated measures to mitigate the spread of this virus amongst which social distancing protocol seems to be the most effective. This study measures compliance with COVID-19 social distancing protocol using Radio Frequency Identification in a university in Southeastern Nigeria. The result reveals low indicators of compliance with the social distancing precautionary measures. The results from different faculties indicate that the students did not comply, with an average compliance of 24 percent. The study contributes to present and future pandemic management as the rate at which the virus spreads despite the precautionary measures were alarming. Hence the need to measure compliance in real time rather than adopting an abstract method in a critical situation of this nature.
Radio frequency identification (RFID) provides real-time network monitoring capabilities for threat identification. However, accurate detection is impeded by tag interference. This paper presents an adaptive collision tree algorithm that selects optimal binary or octal splits based on collision counts to handle interference. Experiments demonstrate an integrated RFID intrusion detection framework that achieves 8.98% higher throughput and 99.82% detection accuracy compared to other protocols. The method enables efficient real-time threat identification as networks proliferate. However, there are limitations to the approach, such as assumptions of fixed tag populations rather than dynamic tags and a lack of field testing. To strengthen the approach, further research on fluctuating tags and validation in real-world network deployments is necessary. This work presents an adaptive method for leveraging RFID to achieve scalable and accurate network intrusion detection.
The research aims at assessing the level of interest towards and adoption of Radio Frequency IDentification (RFID) technology among a sample of manufacturers, retailers, logistic service providers, solution providers and vertically integrated fashion retailers belonging to the Fashion, Consumer Goods and Healthcare sectors in Italy. A survey was created in collaboration with GS1 Italy, a no-profit organization that develops standards for enhancing collaboration between companies, associations, institutions and consumers and enrols more than 40,000 companies. Overall, 58 useful responses were collected. Statistical analyses were carried out with the support of Statistical Package for the Social Sciences (SPSS) for Windows and MS Excel. Results show that RFID solutions are implemented by 31% of the interviewees and tested by 21% of them, mainly thanks to the favourable ROI and the possibility of gaining a competitive advantage over competitors through the automation of logistics processes, visibility of inventory and production progress, and avoided stock out. The RFID users often achieved results above expectations regarding the order picking errors reduction, the increase of operational efficiency and inventory accuracy. The few obstacles encountered are the read failures or the over-performing reading, but they are not perceived as relevant. Overall, most experimenters and non-users currently adopt barcode solutions, but showed their intention to implement RFID systems in the future, underlying a strong interest towards this identification technology.
This study is grounded in the growing significance of environmental sustainability and the widespread adoption of RFID technology across various industries and is aimed to explore the influence of RFID implementation in supply chains by developing a tool that calculates the net balance of CO2 annual emissions. The tool, known as “Return on the Environment” (EROI), is based on a widely accepted environmental assessment method and it calculates the Global Warming Potential (GWP100) incurred and avoided at various stages in the supply chain strictly related to RFID technology introduction. To validate the tool, two RFID deployments have been assessed: one involving a pharmaceutical product tagged on its secondary packaging to monitor the flow of products through the distribution channel, and another a medical device tagged on both primary and secondary packaging to oversee product flow from the supplier distribution center to the hospital operating theatres. In both cases, the results indicate that implementing RFID technology reduced GWP100 compared to the scenarios without RFID. This was primarily due to decreased product shrinkage, lower missing or expired products, and reduced additional transportation due to shipping errors. The tool is versatile and it could be applied to any type of product, serving as a source of inspiration for those who want to assess the sustainability of RFID technology not only from an economic perspective, i.e. ROI calculation, but also from an environmental view. Future work will address the third level of sustainability, RFID social sustainability, that is the impact RFID deployments may have on empowering people, improving staff and employees working conditions, and creating possibilities for high-value job opportunities.
