Localization-Based Beam Focusing in Near-Field Communications

NFC-enabled sensing platform for the onsite determination of asparagine in food

Counterfeit pharmaceuticals remain a major public health challenge, particularly in regions with limited regulatory enforcement and digital traceability systems. This study addresses that challenge by proposing a cryptographically anchored drug traceability framework built on a Directed Acyclic Graph (DAG) ledger for secure, decentralized, and verifiable supply-chain tracking. Unlike conventional blockchain architectures, the DAG structure supports parallel transaction validation, zero transaction fees, and low-latency edge operations, making it suitable for real-time pharmaceutical monitoring in constrained environments. Each drug transaction is represented as a DAG node containing a hashed content identifier (CID), digitally signed metadata, and parent linkages that preserve structural integrity and tamper resistance. Encrypted Near Field Communication (NFC) tags affixed to pharmaceutical packages interact with Aadhaar-linked identities to enable traceable, identity-bound authentication. In this work, NTAG424 DNA tags are employed for secure data exchange, with on-chip encryption and mutual authentication to minimize exposure of key material and mitigate man-in-the-middle attacks. To support offline or rural deployments, the framework integrates an edge-ledger buffering mechanism that ensures eventual DAG synchronization via Merkle-root anchoring. Anomaly risks—such as tag tampering, scan failure, and connectivity interruptions—are predicted using a hybrid deep learning model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) layers trained on synthetic, simulation-generated datasets enriched with environmental and behavioral covariates. In a simulated evaluation encompassing 1,000 pharmaceutical units across five regions, the system achieved 94.5% anomaly detection precision, 0.92 traceability accuracy, and 85 ms median latency. All evaluations were performed in a controlled simulation environment using Docker Swarm–based distributed containers on Raspberry Pi 4B edge devices with IOTA Chrysalis nodes and Grafana analytics dashboards. All reported metrics are consistent with the performance summaries in Table 14 and Fig. 10. Overall, this research demonstrates the simulation-based feasibility of a DAG–NFC framework for secure and interoperable pharmaceutical traceability. The results suggest potential scalability and privacy preservation under controlled conditions, though full operational validation through real-world pilots and regulatory assessment remains an essential next step.

Sparse Array Enabled Near-Field Communications: Beam Pattern Analysis and Hybrid Beamforming Design

The conventional paper-based ticketing system for unreserved travel in Indian Railways is plagued by inefficiencies including significant paper waste, susceptibility to fraud, and prolonged passenger queues. This paper proposes a novel, sustainable solution: a reusable ticketing system leveraging Near Field Communication (NFC) technology, accessible entirely through a web interface. The system utilizes durable NFC cards that can be electronically reset and reused for thousands of journeys, drastically reducing operational costs and environmental impact. A cloud-based backend with offline synchronization ensures reliability in low-connectivity areas, while AES-256 encryption and JWT authentication guarantee security. Accessible via a standard web browser on NFC-enabled devices, this system eliminates the need for dedicated mobile applications, offering a scalable, cost-effective, and secure alternative that enhances both operational efficiency and the passenger experience.

Near-Field Communications: Challenges and Opportunities in the Networking Landscape

Rate-Splitting Multiple Access for Near-Field Communications With Imperfect CSIT and SIC

Analysis on Energy Efficiency of RIS-Assisted Multiuser Downlink Near-Field Communications

Unraveling Spherical Wavefront Complexities: A New Channel Estimation Paradigm for Near-Field Communications via Generative Adversarial Networks

How to Enhance Spectrum Efficiency for Near-Field Communications: From LDMA to NOMA?

LLM-Empowered Near-Field Communications for Low-Altitude Economy

Near-field communication (NFC) is widely used in access control systems such as payment processing and regulating access to facilities. Due to its decentralised nature, NFC is constrained by resource limitations, making it vulnerable to exploits such as key cloning. This study investigated the effectiveness of machine-learning algorithms in visually distinguishing cards as an added security measure against unauthorised cloned cards. The methodology includes collecting datasets, building classification models (CNN, KNN and SVM), performance evaluations and integration of the best-performing model into an NFC prototype, Clone Guard. Performance evaluations included accuracy, precision, F1-score and recall metrics. We found that CNN was the best-performing model, with a prediction accuracy of 96 per cent. Experimental results showed that noisy datasets produced a more robust model than noiseless datasets. Heatmap visualisations indicate that distinct colours and bold text regions contributed significantly to the model’s decision-making. Despite the high accuracy on test data, the prototype performed less accurately when classifying scanned cards. The study provided a basic evaluation of classification algorithms, concluding that deep learning offered greater suitability. The implications of the prototype extended into the applied research domain, offering a configurable and deployable solution to improve the resilience of NFC-based access systems against unauthorised cloned cards.

This paper presents the development, implementation, and evaluation of a computational system for integrating production control, inventory management, and maintenance operations in a manufacturing company using Near Field Communication (NFC) technology. The proposed solution leverages NFC tags as a core element to ensure real-time tracking of products and components throughout the manufacturing process. The system architecture consists of NFC tags for product identification, a custom mobile application for data acquisition, and a centralized database for storage and analysis. The system workflow enables operators to register, monitor, and update the status of products using mobile devices, enhancing traceability, efficiency, and data accuracy. The solution was validated in a real-world manufacturing environment through functional tests and a System Usability Scale (SUS) questionnaire, which yielded an average usability score of 72.08, indicating a good level of user satisfaction. These results demonstrate the feasibility, usability, and practical benefits of the proposed system, which contributes to the digital transformation of industrial operations and aligns with Industry 4.0 principles.

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.

The technology for accessing vehicles has evolved significantly from traditional mechanical keys to advanced keyless systems, utilizing smartphones and smart wearables. The conventional LF-RF-based Passive Entry and Passive Start (PEPS) systems, struggle with inherent vulnerabilities. In particular, they suffer from relay attacks that exploit signal amplification to bypass proximity detection mechanisms. As vehicles become more connected through V2X and other shared mobility ecosystems, securing access systems is more critical than ever. To address these challenges, this research proposes a secured vehicle access framework that combines Bluetooth Low Energy (BLE), Ultra-Wideband (UWB), and Near Field Communication (NFC). Our system introduces multi-layered defense mechanisms, including asymmetric encryption-based digital key applets, dynamic Unique Rolling Session Keys (URSKs), and UWB-based secure ranging, using Time Difference of Arrival (TDOA) and trilateration techniques for precise user localization. BLE is used exclusively for authenticating the legitimate device. Passive unlocking is permitted only after proximity is verified by UWB, ensuring that the user’s device is truly near the vehicle. The development process utilized ANSYS HFSS 3D High Frequency Simulation for dual antenna design which enabled precise calibration. The implementation was achieved using a chipset with an ARM Cortex M33 core with hardware accelerators. Our experiments demonstrated that the system reliably triggers door unlock within only a 1.6 m radius, thus effectively mitigating relay attack risks. This framework offers a robust, future-ready, and user-centric solution for next-generation vehicle access control.

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as 'Watch_TV', 'Sleep', 'Work_On_Computer', and 'Cook_Dinner', is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models-XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)-are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation.

Purpose With rapid advancements in mobile technology, near-field communication (NFC) mobile banking is growing as the preferred option over traditional banking systems. Nevertheless, the adoption rates among senior citizens in developing countries such as India are not encouraging. There is ample scope for innovation and knowledge management activities. This study aims to explore the key motivations (innovation); technology readiness (knowledge management); volitional, nonvolitional and emotional factors affecting senior citizens’ intention to adopt NFC-based mobile banking in India. Design/methodology/approach The study gathered data from 342 senior citizens using NFC-based mobile banking in central India. A mixed-method approach combining the predictive strength of partial least squares–structural equation modeling and the configurational insights of fuzzy-set qualitative comparative analysis was used to test the proposed framework. Findings The analysis reveals the factors other than emotional factors that have significant associations with senior citizens’ adoption of NFC mobile banking. Also, knowledge management and innovation play critical roles in the overall mobile banking adoption ecosystem. Research limitations/implications The present study will contribute to adoption theories by considering senior citizens as respondents. Practitioners will be helped to understand senior citizens’ adoption behavior, and they will be able to formulate strategies effectively for senior citizens. This research contributes to the body of literature relating to innovation and knowledge management. As the study analyzed feedback from Indian respondents, the results may differ if feedback from respondents in other countries is analyzed. Hence, the results lack generalizability. Future researchers are advised to include many more respondents to achieve results with more generalizability. Originality/value The present study considered senior citizens as respondents to analyze the adoption of NFC technologies in mobile banking. Very few studies investigated such issues. Thus, it is a unique research study and contributes to the body of literature from knowledge management and innovation perspectives.

A battery-free wearable sweat lactate sensing patch for assessing muscle fatigue and recovery.

Accelerated repair of Achilles tendon rupture and prevention of re-rupture continue to pose significant technical challenges in orthopedic surgery and rehabilitation. Extracellular vesicles (EVs) derived from bone marrow mesenchymal stem cells exhibit substantial therapeutic potential for various degenerative diseases and tissue regeneration. However, the use of EVs alone for repairing ruptured Achilles tendons requires multiple invasive administrations, such as repeated injections, to maintain a therapeutic effect, which increases patient discomfort and the risk of infection. In this study, we innovatively combined EVs with sodium alginate-based piezoelectric hydrogel (SPH) to develop SPH-EVs. By leveraging the slow degradation of SPH in vivo, SPH-EVs enable sustained-release of EVs while generating electrical stimulation, ensuring that an effective therapeutic concentration is maintained at the Achilles tendon fracture site. Additionally, the integrated near-field communication (NFC) module within SPH-EVs allows for real-time monitoring of rehabilitation exercise intensity in the affected area, guiding patients to conduct rehabilitation training within a safe range and minimizing the risk of re-rupture.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s12951-025-03606-5.

Holographic-Pattern-Based Multiuser Beam Training in RHS-Aided Hybrid Near-Field and Far-Field Communications