Search Results (603)

In the context of increasing resource scarcity and pervasive uncertainty, informed economic decision-making requires access to timely and accurate information. Real-time sustainability monitoring tools, such as sensor- or RFID-based systems, have become essential to capture dynamic changes in production environments. Given the growing importance of sustainability, evaluating the environmental impact of production systems through Life Cycle Assessment (LCA) is critical, while economic dimensions are typically addressed via Life Cycle Costing (LCC). However, conventional LCA and LCC approaches often rely on static or outdated data, limiting their applicability in dynamic environments. This paper presents an integrated framework for the real-time assessment of Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), utilizing Radio Frequency Identification (RFID) technology to facilitate continuous data collection throughout the production chain. By combining environmental and economic assessments with real-time data streams, the proposed framework supports more adaptive, transparent, and sustainable decision-making in resource-constrained industrial contexts. Full article

This paper presents a mobile application designed to support emergency operators during indoor missions, where Global Positioning System (GPS) often fails. The system combines a wearable waist-mounted Inertial Measurement Unit (IMU) with a network of pre-installed Radio Frequency Identification (RFID) tags, enabling robust, real-time geo-referenced tracking of both personnel and critical Points of Interest (PoIs), such as resources and threats. Development was guided by interviews and surveys with emergency professionals, ensuring the tool addresses real operational needs. Key features include dynamic updates of operator positions and nearby hazards, enabled by an Indoor Positioning System (IPS) that fuses IMU and RFID data to improve accuracy in position and heading estimation. The application also offers a user-friendly Human–Environment Interface (HEI) displaying information on a spatially referenced map. By merging advanced technology with expert feedback, this system enhances safety and coordination in critical scenarios, offering a promising solution for indoor navigation and Situational Awareness (SA) in emergency response. Full article

Children with intoeing gait are at increased risk of tripping and consequent injury, reduced mobility, and psychological issues. Quantification of tripping is needed outside the gait lab during daily life for improved clinical assessment and treatment evaluation and to enrich the database for artificial intelligence (AI) learning. This paper presents the development of a low-cost, wearable tripping monitor to log a child’s Tripping Hazard Events (THEs) and steps taken during two weeks of everyday activity. A combination of sensors results in a high probability of THE detection, even during rapid gait, while guarding against false positives and minimizing power and therefore monitor size. A THE is logged when the feet come closer than a predefined threshold during the intoeing foot swing phase. Foot proximity is determined by a Radio Frequency Identification (RFID) reader in “sniffer” mode on the intoeing foot and a target of passive Near-Field Communication (NFC) tags on the contralateral foot. A Force Sensitive Resistor (FSR) in the intoeing shoe sets a time window for sniffing during gait and enables step counting. Data are stored in 15 min epochs. Laboratory testing and an IRB-approved human participant study validated system performance and identified the need for improved mechanical robustness, prompting a redesign of the monitor. A custom Python (version 3.10.13)-based Graphical User Interface (GUI) lets clinicians initiate recording sessions and view time records of THEs and steps. The monitor’s flexible design supports broader applications to real-world activity detection. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

Visibility on roads can be poor during winters owing to snowstorms and other factors. Optical devices, including Light Detection and Ranging devices, are ineffective under whiteout conditions. Moreover, buildings, trees, and other obstacles reduce the accuracy of the Global Positioning System. Therefore, we investigate vehicle navigation using an Ultrahigh Frequency Radio Frequency Identification (RFID) system. This study extends a previously developed RFID-based navigation system for paved roads to unpaved roads. Unpaved roads, particularly those in mountainous or forested areas, can become unstable because of weather conditions and present unique challenges regarding the stability of RFID tags. We use geocells to provide road stability and maintain the RFID tags at the ideal position and attitude. We insert RFID tags into polyvinyl chloride pipe holders and attach them to geocells. We also use the vehicle heading angle from the inertial navigation system (INS). In some areas, the INS is disturbed and shows incorrect direction. We utilize the RFID tag reading history to improve vehicle positioning accuracy by compensating for errors in the INS. Applying this correction reduces the average deviation from the lane center. Driving experiments are conducted on a straight unpaved road, and good results are obtained. These results validate the robustness of the proposed vehicle navigation system, which combines an RFID system with a geocell, providing insights into its successful implementation on unpaved roads. Full article

The construction industry faces persistent productivity challenges despite the widespread adoption of advanced digital technologies. This systematic review examines how digital technologies contribute to improving on-site labor productivity within the Architecture, Engineering, Construction, and Operations (AECOs) sector. Following the PRISMA methodology, 431 records were initially identified, with 28 high-quality articles ultimately selected for analysis through rigorous screening and snowballing techniques. The reviewed technologies include Building Information Modeling (BIM), photogrammetry, LiDAR, augmented reality (AR), global navigation satellite systems (GNSSs), radio frequency identification (RFID), and unmanned aerial vehicles (UAVs), which were categorized into three key areas: factors affecting productivity, modeling and evaluation, and productivity improvement methods. Findings highlight that these technologies collectively enhance resource allocation, reduce labor costs, and improve project scheduling through better coordination. Whilst digital technologies demonstrate substantial impact on construction productivity, further research is needed to quantify long-term benefits and address scalability challenges across different project contexts and organizational structures. Ultimately, the review concludes that digital technologies play a crucial role in enhancing construction productivity, highlighting the need for further research to assess long-term advantages and scalability across diverse construction environments. These technological advancements are essential for modernizing the industry and supporting sustainable growth in the digital transition era. Full article

To address the challenges of dynamic coupling interference and time-frequency feature degradation in current approaches to Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) behavior recognition, this study proposes a novel behavior recognition method integrating multi-feature analysis with a dual-path residual network. The proposed method mitigates interference by using phase difference methods to eliminate signal errors and cross-correlation, as well as adaptive equalization algorithms to decouple interfering signals. To identify the target tags participating in behavioral interactions, we construct a three-dimensional feature space and apply an improved weighted isolated forest algorithm to detect active tags during interactions. Subsequently, Doppler shift analysis extracts behavioral features, and multiscale wavelet-packet decomposition generates time-frequency representations. The dual-path residual network then fuses global and local features from these time-frequency representations for behavioral classification, thereby identifying interaction behaviors such as ‘taking away’, ‘putting back’, and ‘hesitation’ (characterized by picking up, then putting back). Experimental results demonstrate that the proposed scheme achieves behavioral recognition accuracy of 94% in complex scenarios, significantly enhancing the overall robustness of interaction behavior recognition. Full article

Individual cow identification is a prerequisite for automatically monitoring behavior patterns, health status, and growth data of each cow, and can provide the assistance in selecting excellent cow individuals for breeding. Despite high recognition accuracy, traditional implantable electronic devices such as RFID (i.e., Radio Frequency Identification) can cause some degree of harm or stress reactions to cows. Image-based methods are widely used due to their non-invasive advantages, but these methods have poor adaptability to different environments and target size, and low detection accuracy in complex scenes. To solve these issues, this study designs a Dy_Conv (i.e., dynamic convolution) module and innovatively constructs a Dynamic_Bottleneck module based on the Dy_Conv and S2Attention (Sparse-shift Attention) mechanism. On this basis, we replaces the first and fourth bottleneck layers of Resnet50 with the Dynamic_Bottleneck to achieve accurate extraction of local features and global information of cows. Furthermore, the QAConv (i.e., query adaptive convolution) module is introduced into the front end of the backbone network, and can adjust the parameters and sizes of convolution kernels to adapt to the scale changes in cow targets and input images. At the same time, NAM (i.e., normalization-based attention module) attention is embedded into the backend of the network to achieve the feature fusion in the channels and spatial dimensions, which contributes to better distinguish visually similar individual cows. The experiments are conducted on the public datasets collected from different cowsheds. The experimental results showed that the Rank-1, Rank-5, and mAP metrics reached 96.8%, 98.9%, and 95.3%, respectively. Therefore, the proposed model can effectively capture and integrate multi-scale features of cow body appearance, enhancing the accuracy of individual cow identification in complex scenes. Full article

Precision livestock farming (PLF) is rapidly advancing, with a growing array of technologies being explored and implemented to improve both productivity and animal welfare. One of the major challenges in this field is the identification of individual animals. Despite numerous efforts having been made to automate this process, there remains a lack of holistic reviews that comprehensively integrate and evaluate these technological developments. Historically, humans have employed various techniques to identify individual animals. This article provides an overview of the evolution of animal identification methods, highlighting significant transitions across various time periods. In prehistoric times, identification relied solely on visual inspection. Today, advanced methods are being utilized, such as radio frequency identification (RFID), computer vision-based systems, biometric recognition, and DNA profiling. Each identification method has its own strengths and limitations. Interestingly, early methods such as visual inspection and drawing can still inspire the development of novel automated systems when combined with modern technologies. Full article

In the field of the Internet of Things (IoT), technical solutions that enable information transmission with minimal energy consumption are of particular interest. Common solutions frequently used in the field of radio frequency identification (RFID) involve utilizing electromagnetic waves to power tags and employing backscattering for communication. Detecting the received signal in a coherent manner enables increased reliability in tag reading. This paper proposes a method for coherent signal detection in a bistatic backscatter communication system (BBCS), which includes coarse carrier frequency offset (CFO) from preamble and fine phase correction from data symbols. The proposed method outperforms the detection approach based on maximum likelihood estimation (MLE) of CFO from the preamble, particularly in scenarios with higher CFO values. The proposed detection method is well suited for implementation in software-defined radios, particularly in low-cost devices characterized by less stable oscillators. It is also shown that a preamble of six symbols is sufficient to perform a coarse CFO estimation. Since the analyzed system is equivalent to binary frequency-shift keying (FSK) modulation, the performance of FSK is presented as the theoretical upper bound in the results. Full article

Radio frequency identification (RFID) is popular and attaining momentum in manifold sectors, including, but not limited to, pharmaceuticals, retail, defense, transport, healthcare and currently security. Utilizing RFID solely as a solution does not result in effective security. Conventional systems have integrated only one solution, such as GSM, cryptography, wireless sensors, biometrics or a One-Time Password (OTP); however, the security provided is limited since each incorporated technology has its disadvantages. Our paper proposes improving the conventional methods in the field by proposing an intelligent door-lock system prototype implementing two-step authentication, providing double-layered security provisions in, for instance, highly sensitive zones. The suggested technique, firstly based on RFID technology and then a password (PIN) during the authentication process, results in a hybrid system that is more accurate and efficient compared to a traditional, single-method system. The Arduino micro-controller is interfaced with RFID, with a keypad that receives the input to the micro-controller, a Liquid Crystal Display to output the authentication status and finally a motor connected to the door for automation within a limited time-frame. Adding biometric verification, such as fingerprints and face recognition, can enhance the proposed design further by providing an additional layer of security from external intruders. Full article

Background: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a strategic vision, a flexible organizational culture, and the ability to support decisions through artificial intelligence (AI)-based systems. Methods: This study proposes an extended conceptual model using structural equation modelling (SEM) to explore the relationships between five constructs: technological change, strategic and organizational readiness, transformation environment, AI-enabled decision configuration, and operational redesign. The model was validated based on a sample of 217 active logistics specialists, coming from sectors such as road transport, retail, 3PL logistics services, and manufacturing. The participants are involved in the digitization of processes, especially in activities related to operational decisions and sustainability. Results: The findings reveal that the analysis confirms statistically significant relationships between organizational readiness, transformation environment, AI-based decision processes, and operational redesign. Conclusions: The study highlights the importance of an integrated approach in which technology, organizational culture, and advanced decision support collectively contribute to the transition to digital and circular logistics chains. Full article

Food loss and waste from the European Union’s dairy supply chain, particularly in the management of fresh milk, imposes significant environmental burdens. This study demonstrates that implementing Radio Frequency Identification (RFID)-enabled digital decision-support tools can substantially reduce these impacts across the region. A cradle-to-grave life cycle assessment (LCA) was used to quantify both the additional environmental burdens from RFID (tag production, usage, and disposal) and the avoided burdens due to reduced milk losses in the farm, processing, and distribution stages. Within the EU’s fresh milk supply chain, the implementation of digital tools could result in annual net reductions of up to 80,000 tonnes of CO₂-equivalent greenhouse gas emissions, 81,083 tonnes of PM_2.5-equivalent particulate matter, 84,326 tonnes of land use–related carbon deficit, and 80,000 cubic meters of freshwater-equivalent consumption. Spatial analysis indicates that regions with historically high spoilage rates, particularly in Southern and Eastern Europe, see the greatest benefits from RFID enabled digital-decision support tools. These environmental savings are most pronounced during the peak months of milk production. Overall, the study demonstrates that despite the environmental footprint of RFID systems, their integration into the EU’S dairy supply chain enhances transparency, reduces waste, and improves resource efficiency—supporting their strategic value. Full article

Radio Frequency Identification (RFID) technology is widely applied in various scenarios, including logistics tracking, supply chain management, and target monitoring. In these contexts, the malicious cloning of legitimate tag information can lead to sensitive data leakage and disrupt the normal acquisition of tag information by readers, thereby threatening personal privacy and corporate security and incurring significant economic losses. Although some efforts have been made to detect cloning attacks, the presence of missing tags in RFID systems can obscure cloned ones, resulting in a significant reduction in identification efficiency and accuracy. To address these problems, we propose the block-based cloned tag identification (BCTI) protocol for identifying cloning attacks in the presence of missing tags. First, we introduce a block indicator to sort all tags systematically and design a block mechanism that enables tags to respond repeatedly within a block with minimal time overhead. Then, we design a superposition strategy to further reduce the number of verification times, thereby decreasing the execution overhead. Through an in-depth analysis of potential tag response patterns, we develop a precise method to identify cloning attacks and mitigate interference from missing tags in probabilistic cloning attack scenarios. Moreover, we perform parameter optimization of the BCTI protocol and validate its performance across diverse operational scenarios. Extensive simulation results demonstrate that the BCTI protocol meets the required identification reliability threshold and achieves an average improvement of $24.01\%$ in identification efficiency compared to state-of-the-art solutions. Full article

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article