IEEE journal of radio frequency identification最新文献

Stretchable Antennas for Wearable Application: Materials Guideline With Design Considerations 可穿戴应用的可拉伸天线：材料指南与设计考虑

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-03-06 DOI: 10.1109/JRFID.2026.3671254

Jen-Hahn Low;Eng-Hock Lim;Pei-Song Chee

引用次数: 0

AI-Enabled IoT for Hybrid Ultrasonic–RFID Indoor Positioning 基于ai的物联网超声- rfid混合室内定位

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-03-05 DOI: 10.1109/JRFID.2026.3670739

Ignazio Tripodi;Giuseppe Martino;Demetrio Iero;Riccardo Carotenuto;Massimo Merenda

{"title":"AI-Enabled IoT for Hybrid Ultrasonic–RFID Indoor Positioning","authors":"Ignazio Tripodi;Giuseppe Martino;Demetrio Iero;Riccardo Carotenuto;Massimo Merenda","doi":"10.1109/JRFID.2026.3670739","DOIUrl":"https://doi.org/10.1109/JRFID.2026.3670739","url":null,"abstract":"Indoor positioning is a key enabler for many Internet of Things (IoT) applications, where high accuracy, low power consumption, and limited infrastructure cost must coexist. This paper presents a fully embedded implementation and experimental validation of the ultrasonic–RFID hybrid indoor localization architecture previously introduced in Merenda et al., 2022, in which standard UHF RFID infrastructure simultaneously provides synchronization and a backhaul channel for position data, while an ultrasonic front-end performs time-of-flight ranging. A microcontroller-based Beacon unit sequentially emits linear up-chirp signals from four ceiling-mounted transducers, and a mobile device equipped with an ultrasonic MEMS microphone acquires the waveform and extracts four peak-time features. These features feed a lightweight multi-output regression neural network deployed on the same microcontroller via an embedded machine learning workflow. The model is trained on a dataset of more than 32k frames acquired with a 6-DoF robotic arm in a Vicon-instrumented laboratory, using sub-millimeter optical ground truth as supervision. Experimental results show that the proposed edge implementation achieves a mean Euclidean positioning error of 7.83 cm, with 95% of estimates within 15.13 cm, while providing accuracy comparable to a traditional cross-correlation-based multilateration pipeline but with significantly reduced computational load, enabling update rates close to 10 Hz with millisecond-level latency. The resulting architecture is fully battery-powered, scalable, and suitable for AI-enabled IoT indoor positioning scenarios.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"10 ","pages":"176-187"},"PeriodicalIF":3.4,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LPWAN Smart Waste Bin With On-Device AI Trained on Synthetic Data LPWAN智能垃圾箱与设备上的人工智能在合成数据训练

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-02-24 DOI: 10.1109/JRFID.2026.3667562

Pavel Trávníček;Václav Nežerka

{"title":"LPWAN Smart Waste Bin With On-Device AI Trained on Synthetic Data","authors":"Pavel Trávníček;Václav Nežerka","doi":"10.1109/JRFID.2026.3667562","DOIUrl":"https://doi.org/10.1109/JRFID.2026.3667562","url":null,"abstract":"The construction industry is a major contributor to global solid waste, yet circular economy initiatives are often impeded by inefficient logistics and improper sorting. Existing monitoring solutions, typically reliant on single-point distance sensors, lack the granularity to identify waste composition, which is essential for effective valorization. This work proposes an energy-efficient, image-based smart bin system enabled by Low Power Wide Area Networks (LPWAN) that utilizes on-device Artificial Intelligence (AI) to simultaneously estimate fill levels and classify waste materials. To address the scarcity of labeled field data, a synthetic data generation strategy using generative AI was employed to create photorealistic training datasets. A lightweight MobileNetV2 model was optimized via quantization and deployed on an ESP32 microcontroller. The system architecture prioritizes energy conservation by performing inference at the edge and transmitting only compact results, reserving full image transmission for a closed-loop active learning pipeline. Energy profiling demonstrated that on-device inference drastically reduces active radio time compared to raw image streaming, significantly extending battery life. The work validates the feasibility of Edge AI for scalable construction and demolition waste monitoring and highlights the potential of synthetic data to overcome data scarcity bottlenecks.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"10 ","pages":"150-161"},"PeriodicalIF":3.4,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408857","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Council on RFID IEEE射频识别委员会

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-02-23 DOI: 10.1109/JRFID.2026.3660075

引用次数: 0

IEEE Journal of Radio Frequency Identification Publication Information IEEE射频识别杂志出版信息

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-02-23 DOI: 10.1109/JRFID.2026.3660073

引用次数: 0

An RFID-Augmented Information Retrieval Technique for AI-Enabled IoT Devices Ensuring Agri-Food Traceability and Anti-Counterfeiting 用于人工智能物联网设备的rfid增强信息检索技术，确保农业食品的可追溯性和防伪

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-02-11 DOI: 10.1109/JRFID.2026.3663762

Mohamed Riad Sebti;Alberto Arciello;Mariateresa Russo;Massimo Merenda

{"title":"An RFID-Augmented Information Retrieval Technique for AI-Enabled IoT Devices Ensuring Agri-Food Traceability and Anti-Counterfeiting","authors":"Mohamed Riad Sebti;Alberto Arciello;Mariateresa Russo;Massimo Merenda","doi":"10.1109/JRFID.2026.3663762","DOIUrl":"https://doi.org/10.1109/JRFID.2026.3663762","url":null,"abstract":"Globalized agri-food supply chains face rising risks of traceability gaps and product fraud, especially during early production stages where environmental conditions affect safety and quality. This work introduces a compact and low-cost, power efficient MCU-based Edge-AI system that enhances real-time traceability and anti-counterfeiting from harvest to consumer. Environmental data are collected during harvesting, and the information saved into the RFID tag is generated directly from these data through an embedded AI model, enabling local, connectivity-free decision-making. The system is designed to run efficiently on microcontrollers, making it fully deployable in field conditions. The resulting information is securely stored in the RFID tag, which acts as a portable, tamper-evident data carrier. A dual-scan mechanism and backend anchoring ensure authenticity, while a blockchain layer provides immutable record-keeping in a simulated environment. A prototype applied to grape harvesting demonstrates high model accuracy, fast on-device inference, low power usage, reliable RFID operations, and ease of integration with a web-based traceability platform. These results show that combining Edge-AI with RFID and blockchain provides a scalable and practical solution for improving transparency and protecting agri-food products against counterfeiting.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"10 ","pages":"122-135"},"PeriodicalIF":3.4,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11392767","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of UHF RFID Tag Antennas Using an Equivalent Circuit Approach 基于等效电路的超高频RFID标签天线分析

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-02-09 DOI: 10.1109/JRFID.2026.3662603

Pavel Nikitin;John Kim;K. V. S. Rao

引用次数: 0

Intrinsic Physical Unclonable Function (PUF) Based on Laser-Induced-Graphene (LIG) UHF-RFID Antennas 基于激光诱导石墨烯（LIG）超高频射频识别天线的固有物理不可克隆函数（PUF）

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-01-28 DOI: 10.1109/JRFID.2026.3658621

Francesca M. C. Nanni;Gaetano Marrocco

{"title":"Intrinsic Physical Unclonable Function (PUF) Based on Laser-Induced-Graphene (LIG) UHF-RFID Antennas","authors":"Francesca M. C. Nanni;Gaetano Marrocco","doi":"10.1109/JRFID.2026.3658621","DOIUrl":"https://doi.org/10.1109/JRFID.2026.3658621","url":null,"abstract":"The pursuit of eco-sustainable materials for next-generation radio-frequency and sensing devices is fostering the development of antennas and tags that are not only recyclable but also capable of ensuring physical-level authenticity. Among these, Laser-Induced Graphene (LIG) enables the direct fabrication of conductive structures on polymeric substrates. Its intrinsic fabrication variability introduces microscopic randomness that can be exploited to realize Physical Unclonable Functions (PUFs) for secure authentication. This work proposes a near-field interrogation and processing framework for LIG-based UHF-RFID antennas, enabling the extraction of digital cryptographic keys from the electromagnetic backscattered response. A dedicated signal-processing and binarization pipeline converts analog amplitude and I/Q responses into statistically balanced binary databases. An experimental campaign on 30 devices, tested with two reader antennas and two orthogonal orientations, demonstrates that the proposed method can generate up to 76 independent bits per key, with reliability exceeding 0.8 and decidability values up to 8, ensuring both repeatability and device-level distinctiveness. By varying frequency, power, and orientation, multiple uncorrelated key families can be generated.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"10 ","pages":"101-112"},"PeriodicalIF":3.4,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metal Mountable UHF RFID Tag Design Requiring Screw Fixing 金属安装超高频RFID标签设计需要螺丝固定

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-01-19 DOI: 10.1109/JRFID.2026.3655933

Daniele Inserra;Yang Wang;Fengwei Peng;Zhenbing Li;Guangjun Wen

引用次数: 0

2025 Index IEEE Journal of Radio Frequency Identification 2025索引IEEE射频识别杂志

IF 3.4

IEEE journal of radio frequency identification Pub Date : 2026-01-16 DOI: 10.1109/JRFID.2026.3655543

引用次数: 0