Search Results (223)

Radio Frequency Identification (RFID) is a fundamental enabling technology for the Internet of Things (IoT), providing automatic identification and data retrieval capabilities for various applications. With the increasing prevalence of ubiquitous and mobile computing, serverless RFID systems have attracted significant attention due to their elimination of the need for continuous connection to a centralized backend database. However, most existing RFID authentication protocols either rely on backend server involvement or are unable to simultaneously withstand denial-of-service (DoS) attacks, tracking attacks, reader intrusion attacks, and desynchronization attacks. To overcome these limitations, this paper proposes a lightweight authentication protocol suitable for serverless RFID environments. This scheme utilizes dynamic key updates, random numbers, and one-way hash functions to achieve bidirectional authentication between RFID tags and portable readers without the need for a backend server during the authentication process. Furthermore, this paper introduces a dual-key synchronization mechanism to maintain consistency between communicating entities and effectively prevent desynchronization attacks caused by message interception, loss, or replay. Security analysis shows that the proposed protocol meets all major RFID security requirements, including resistance to eavesdropping, tag cloning, identity impersonation, tracking, privacy breaches, denial-of-service attacks, reader intrusion attacks, and desynchronization attacks. Compared to typical RFID authentication protocols, this scheme is the only one that can simultaneously support serverless RFID operation and resist DoS attacks and reader intrusion attacks. Furthermore, the protocol requires only lightweight hash calculations and three rounds of communication, significantly reducing communication overhead compared to traditional four-round RFID authentication protocols. Performance analysis shows that the scheme maintains low computational complexity, storage requirements, and communication costs, making it suitable for resource-constrained RFID tags. The results demonstrate that the proposed protocol achieves an effective balance between security, efficiency, and deployment flexibility, making it a practical solution for next-generation serverless RFID applications in the IoT environment. Full article

Accurate per-wagon occupancy accounting at freight stations—knowing which wagon entered or exited which track and when—is a prerequisite for automated shunting management, yet existing technologies—axle counters, RFID, computer vision, and LPWAN IoT—each provide only a subset of the required information and depend on dedicated infrastructure or favourable conditions. This paper investigates whether two fixed BLE gateways, combined with Eddystone-TLM beacon nodes proposed for mounting on freight wagon bodies, can classify passage direction from RSSI signals without supervised model training or labelled training data, site-specific measurement campaigns, or track modification. The enabling mechanism is wagon-body attenuation: as a wagon passes between the receivers, its metallic body creates a temporal asymmetry in the RSSI envelopes that encodes travel direction. We present a five-stage online pipeline at O (1) memory per packet: a two-sided CUSUM detector with adaptive per-event baseline estimation segments the RSSI stream; a three-stage validation filter rejects partial passes, lateral paths, and near-gateway reversals; and direction is classified by the normalised Temporal Centroid shift—a speed-invariant feature requiring no training data—with a cascade fallback for ambiguous short windows. Combined with the beacon MAC address as a wagon identifier, the system generates structured occupancy events directly consumable by station management systems. Validated on 151 labelled events across eight scenario categories at Urtaul freight station and the TSTU test polygon, the pipeline achieves 96.7% accuracy (95% Wilson CI: [92.5%, 98.6%]) and zero wrong-direction predictions across all 84 directional events (exact Clopper-Pearson 95% CI for the wrong-direction rate: [0%, 3.5%]); a Random Forest baseline on the same features confirms supervised learning adds no measurable benefit over the training-free approach within this feature space. The validation was conducted on 151 isolated single-wagon events collected under dry-weather conditions at two sites using a fixed 15 m gateway spacing; multi-wagon scenarios and adverse environmental conditions remain topics for future work. Full article

Radio Frequency Identification (RFID) is an emerging technology in Industry 4.0 for low-cost logistics, yet direction and orientation estimation typically requires multiple antennas, and robustness under industrial multipath fading, operator variability, and signal fragmentation has not been evaluated. To address this gap, this study proposes a single-antenna RFID system that evaluated thirteen architectures spanning unsupervised methods (clustering algorithms) and supervised methods (classical machine learning, deep learning, and hybrid architectures) on Received Signal Strength Indicator (RSSI) and phase time-series reconstructed through a pipeline of Savitzky–Golay smoothing, phase unwrapping, and cubic spline resampling to $N$ = 50–300 samples, preserving signal morphology across variable-length RFID passes. The system further incorporates a physics-informed augmentation strategy that encodes multipath fading, distance variation, and fragmentation into synthetic training samples for cross-domain generalization without hardware modification. In controlled laboratory experiments, both direction and orientation tasks achieved $>$99.5% accuracy, while direction tracking was additionally validated on an industrial shop floor under varying distances, Non-Line-of-Sight (NLoS) occlusions, and signal fragmentation. Zero-shot transfer caused accuracy to degrade to near-chance levels for several configurations, confirming a pronounced domain gap. Domain adaptation with XGBoost recovered direction accuracy to $>$97% under severe fragmentation under NLoS conditions, with an inference latency of ≈150 $\mu$s. Under domain-adapted shop floor conditions, direction accuracy exceeded the 75–92% reported in prior single-antenna laboratory studies, suggesting that physics-informed domain adaptation is a promising approach for single-antenna RFID tracking in Industrial Internet of Things (IIoT) logistics environments. Full article

Background: Healthcare inventory management is critical for ensuring timely access to supplies and reducing stockouts. As supply chains grow more complex, algorithms, AI, and analytics techniques have emerged as tools for forecasting, tracking, classification, and procurement. Yet empirical validation across diverse contexts remains inadequate, and existing reviews treat these approaches as separate streams rather than an integrated system. Methods: To evaluate these capabilities, a systematic review of 64 peer-reviewed articles published between 2011 and 2025 was conducted using a descriptive and content analysis approach on the use of Triple A (Analytics, AI, and Algorithms) techniques in inventory frameworks across various healthcare contexts, such as hospitals, pharmaceutical supply chains, and humanitarian supply chains. Results: Integrating multiple Triple A approaches consistently outperforms single-method strategies, particularly with RFID and IoT tools. Key findings often overlooked are: emergency procurement and classification, which remain neglected despite the highest patient safety stakes, and key procurement drivers—organizational conditions, supplier reliability, and team capacity. Data quality, interoperability, and cybersecurity further constrain generalizability. Conclusions: Bridging these gaps requires integrated Triple A approaches rather than single methods. Phased implementation, cloud-based platforms, and privacy-by-design offer practical pathways for building resilience under real-world constraints. Full article

This paper proposes a SIM-compatible hardware coordination architecture for secure radio-frequency (RF)-triggered activation in mobile devices. The proposed concept functions as a passive coordination layer rather than as an additional wireless transceiver, enabling controlled interaction between external low-frequency RFID or high-frequency NFC fields and wireless subsystems already available in the host device. The architecture assumes a flexible nano-SIM-compatible form factor integrating passive RF detection structures, a trusted decision component, and a trigger-generation interface aligned with standard SIM/UICC electrical and logical interaction models. Upon detection of an external electromagnetic field, the coordination layer evaluates predefined authorization conditions and produces a controlled trigger event intended to propagate through existing telephony and system-service pathways. In contrast to architectures that embed active wireless transmitters, the proposed approach seeks to minimize hardware redundancy and reduce potential attack surfaces by relying on the host device’s native Bluetooth Low Energy (BLE) capabilities. Rather than directly controlling wireless modules, the interface operates as a hardware-originated coordination mechanism that may support low-power and context-aware activation scenarios in mobile and embedded environments. This paper focuses on the architectural model, system assumptions, security rationale, and implementation constraints of such a SIM-compatible interface. Particular attention is given to integration considerations related to smartphone baseband architectures, operating-system mediation, and secure-element isolation. The presented concept establishes a foundation for future prototype implementation and platform-specific validation of SIM-compatible RF-triggered coordination mechanisms. Full article

By 2050, the global population is expected to reach approximately 10 billion, leading to a projected 50% increase in food demand relative to 2013 levels. If not adequately anticipated, this growing demand will place significant strain on agri-food systems worldwide, with disproportionate impacts on low- and middle-income countries. Moreover, current projections may underestimate the accelerating effects of climate change, political instability, and civil unrest, which continue to disrupt food production and distribution systems. In this context, technological advancements offer a promising pathway to enhance efficiency, improve transparency, and mitigate risks related to food safety, adulteration, and counterfeiting. Emerging innovations can decouple food production from environmental degradation while strengthening monitoring, verification, and accountability across supply chains. This review examines state-of-the-art technologies developed to support traceability and anti-counterfeiting in agri-food supply chains, considering their application across the full spectrum of stakeholders. To provide a system-level perspective, the review adopts a five-layer socio-technical traceability and anti-counterfeiting framework, comprising identity, sensing, intelligence, integrity, and interaction layers, which is used to map enabling technologies and reinterpret the evolution of traceability systems (TS 1.0–TS 4.0) as a progression of functional capabilities rather than isolated technological upgrades. Using this framework, the review analyzes the advantages and limitations of current solutions and clarifies how traceability and anti-counterfeiting functions emerge through technology integration. It further identifies gaps that hinder large-scale and equitable adoption. Finally, future research directions are outlined to address current technical, economic, and governance challenges and to guide the development of more resilient, trustworthy, and sustainable agri-food traceability systems. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

This work presents a curated dataset of multimodal sensor measurements from Internet of Things (IoT) units mounted on waste collection vehicles. Each unit records multiple data streams including GPS position, vehicle velocity, radar-based container presence, accelerometer readings of the lifting arm, and RFID tag identifiers of the bins. The dataset provides two complementary forms of annotation: (1) algorithmically generated events that were manually cleaned through visual inspection of sensor signals, offering large-scale coverage across 5 vehicles over a total of 25 collection days, and (2) manually validated events derived from synchronized video recordings, representing ground truth for 3 vehicles over 8 collection days. In total, the dataset contains 12,391 annotated waste collection events. The dataset spans diverse operational conditions with varying container sizes and includes both RFID-equipped and non-RFID bins. It can be used to train and evaluate machine learning models for event detection, anomaly recognition, or explainability studies, and to support practical applications such as Pay-as-you-throw (PAYT) waste management schemes. By combining multimodal sensor signals with reliable annotations, the dataset represents a unique resource for advancing research in smart waste collection and the broader field of IoT-enabled urban services. Full article

Olive oil is a high-value product that is highly exposed to fraud, making robust traceability systems essential to protect authenticity, consumer trust, and competitiveness. This study examines how digital traceability technologies influence fraud mitigation and the sustainable performance of olive oil mills in southern Brazil. A systematic literature review, conducted according to the PRISMA 2020 protocol in Scopus and Web of Science, identified state-of-the-art supply chain and authentication technologies, including blockchain, IoT, RFID, QR codes, cloud computing, Big Data, artificial intelligence, and physicochemical methods. Two structured questionnaires were then applied to managers from nine mills in the main Brazilian olive oil cluster, and the data were analyzed using descriptive statistics, Chi-Square tests, and correlation measures within a framework grounded in Resource-Based View and Institutional Isomorphism theories. The results show that adoption of digital traceability is still incipient, while internal factors such as organizational commitment and marketing strategies play a more decisive role than external pressures in explaining adoption. Although managers do not yet perceive a direct impact on fraud mitigation, adoption is positively associated with economic, environmental, and social sustainability outcomes. Given the exploratory design and the small, non-probabilistic sample (n = 9), the findings should be interpreted as indicative rather than definitive. The proposed framework is intended as a transferable analytical lens that can be adapted and further validated in other agri-food and industrial contexts using larger samples and objective fraud-related indicators. Full article

This paper addresses the core conflict between long-range communication and ultra-low power requirements in sensing nodes for Wireless Sensor Networks (WSNs) by proposing a wake-up receiver (WuRx) design featuring nanowatt-level power consumption and high sensitivity. Conventional architectures are plagued by low energy efficiency, poor demodulation reliability, and insufficient clock synchronization accuracy, which hinders their practical application in real-world scenarios like WSNs. The proposed design employs an event-triggered mechanism, where a continuously operating, low-power WuRx monitors the channel and activates the main system only after validating a legitimate command, thereby significantly reducing standby power. At the system design level, a key innovation is direct conjugate matching between the antenna and a multi-stage rectifier, replacing the traditional 50 Ohm interface, which substantially improves energy transmission efficiency. Furthermore, a mean-detection demodulation circuit is introduced to dynamically generate an adaptive reference level, effectively overcoming the challenge of discriminating shallow modulation caused by signal saturation in the near-field region. At the baseband processing level, a configurable fault-tolerant correlator logic and a data-edge-triggered clock synchronization circuit are designed, combined with oversampling techniques to suppress clock drift and enhance the reliability of long data packet reception. Fabricated in a TSMC 0.18 µm CMOS process, the receiver features an ultra-low power consumption of 305 nW at 0.5 V and a high sensitivity of −47 dBm, enabling a communication range of up to 400 m in the 920–925 MHz band. Through synergistic innovation at both the circuit and system levels, this research provides a high-efficiency, high-reliability wake-up solution for long-range WSN nodes, effectively promoting the large-scale application of WSN technology in practical deployments. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

RFID (Radio Frequency Identification) technology has become an essential instrument in numerous industrial sectors, enhancing process efficiency and streamlining operations, allowing for the automated tracking of goods and equipment without the need for manual intervention. Nevertheless, the deployment of industrial IoT systems necessitates the establishment of complex sensor networks to enable detailed multi-parameter monitoring of items. Despite these advancements, challenges remain in item-level sensing, data analysis, and the management of power consumption. To mitigate these shortcomings, this study presents a holistic AI-assisted, semi-passive RFID-integrated multi-sensor system designed for robust food quality monitoring. The primary contributions are threefold: First, a compact (45 mm ∗ 38 mm) semi-passive UHF RFID tag is developed, featuring a rechargeable lithium battery to ensure long-term operation and extend the readable range up to 10 m. Second, a dedicated IoT cloud platform is implemented to handle big data storage and visualization, ensuring reliable data management. Third, the system integrates machine learning algorithms (LSTM) to analyze sensing data for real-time food quality assessment. The system’s efficacy is validated through real-world experiments on food products, demonstrating its capability for low-cost, long-distance, and intelligent quality control. This technology enables low-cost, timely, and sustainable quality assessments over medium and long distances, with battery life extending up to 27 days under specific conditions. By deploying this technology, quantified food quality assessment and control can be achieved. Full article

Effective management of chemical reagents in universities is essential for laboratory safety and operational efficiency. Manual management models characterized by fragmented oversight are insufficient to ensure traceability, real-time monitoring, and safety compliance, as evidenced by the recurring occurrence of laboratory safety accidents. In this study, we propose an intelligent management model for college-level chemical reagent repositories. The model was built on a Laboratory Information Management System (LIMS)-based architecture and modified using Internet of Things (IoT) sensing, Radio Frequency Identification (RFID), and intelligent hardware. It transforms the full-lifecycle of reagents (from procurement and storage to distribution, usage, and waste disposal) into a digital, automated, closed-loop process. In addition, this study also highlights key technical challenges, including heterogenous system integration and reliable data acquisition under complex environmental conditions, and proposes practical strategies, such as lightweight Application Programming Interface (API) middleware. The results show that the proposed model is a feasible and robust framework for precise, proactive, and data-driven management of hazardous chemicals in academic settings. Full article

The convergence of Internet of Things (IoT), embedded microcontrollers, and robotics has significantly transformed industrial and service applications under the Industry 5.0 paradigm. IoT-enabled automation not only reduces human intervention but also improves system efficiency, safety, and adaptability across multiple domains. The growing integration of automation technologies in manufacturing lines has significantly reduced human intervention while improving productivity and operational safety. Robotic arms play a crucial role in modern industrial environments, particularly for repetitive, hazardous, or precision-demanding tasks. This study presents a cost-effective robotic arm system for product selection, sorting and processing in automated production lines. The system operates in both automatic and manual modes and utilizes an ESP32-based controller, radio frequency identification (RFID) modules, and low-cost sensors to identify and transport products on a conveyor. A mobile, IoT-enabled interface provides remote real-time monitoring and control, while integrated safety mechanisms, current-voltage protections, and emergency stop circuitry enhance operational reliability. Using cost-effective components to reduce total cost, the system has been successfully validated through experiments to reduce labor dependency and operational errors, proving its scalability and economic viability for industrial automation. Compared to similar systems, this study presents an Industry 5.0 approach for low-cost IoT-based automated production lines. Full article

This paper presents a ubiquitous, blockchain-based system designed to improve transparency, traceability and trust in supply chains involving autonomous vehicles (AVs). The framework integrates Internet of Things (IoT) sensors, radio-frequency identification (RFID) and QR identifiers, global positioning system (GPS) tracking, and mobile communications with smart contracts implemented on the Ethereum 2.0 blockchain. The main contributions are as follows: (1) an architecture enabling real-time monitoring and automated verification of logistics transactions; (2) a proof of concept integrating blockchain, the IoT and Android-based OBUs; and (3) a quantitative analysis of gas and smart contract execution costs. Experimental tests show gas consumption ranging from 21,000 to 5,000,000 units and transaction costs ranging from 0.0001 to 0.0033 ETH, confirming the system’s technical feasibility and cost-efficiency. As well as cost and efficiency, the process improved transparency, real-time traceability and decentralized verification, confirming the system’s efficacy for supply chains involving autonomous vehicles. Full article