Pub Date : 2024-12-19DOI: 10.1109/JRFID.2024.3515612
Yutong Wang;Xiao Wang;Gisele Bennett;Fei-Yue Wang
{"title":"Guest Editorial of the Special Issue on the 3rd IEEE International Conference on Digital Twins and Parallel Intelligence (IEEE DTPI 2023)","authors":"Yutong Wang;Xiao Wang;Gisele Bennett;Fei-Yue Wang","doi":"10.1109/JRFID.2024.3515612","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3515612","url":null,"abstract":"","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"860-864"},"PeriodicalIF":2.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875130","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}
Pub Date : 2024-12-11DOI: 10.1109/JRFID.2024.3514454
Kartik Patel;Junbo Zhang;John Kimionis;Lefteris Kampianakis;Michael S. Eggleston;Jinfeng Du
Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in wide-area applications. An alternative bi-static backscatter radio architecture, characterized by a dis-aggregated illuminator and receiver, can provide enhanced coverage and, thus, can support wide-area applications. In this paper, we analyze the scalability of the bi-static backscatter radio for large-scale wide-area IoT networks consisting of a large number of unsynchronized, receiver-less tags. We introduce the Tag Drop Rate (TDR) as a measure of reliability and develop a theoretical framework to estimate TDR in terms of the network parameters. We show that under certain approximations, a small-scale prototype can emulate a large-scale network. We then use the measurements from experimental prototypes of bi-static backscatter networks (BNs) to refine the theoretical model. Finally, based on the insights derived from the theoretical model and the experimental measurements, we describe a systematic methodology for tuning the network parameters and identifying the physical layer design requirements for the reliable operation of large-scale bi-static BNs. Our analysis shows that even with a modest physical layer requirement of bit error rate (BER) 0.2, 1000 receiver-less tags can be supported with 99.9% reliability. This demonstrates the feasibility of bi-static BNs for large-scale wide-area IoT applications.
{"title":"Analyzing the Scalability of Bi-Static Backscatter Networks for Large Scale Applications","authors":"Kartik Patel;Junbo Zhang;John Kimionis;Lefteris Kampianakis;Michael S. Eggleston;Jinfeng Du","doi":"10.1109/JRFID.2024.3514454","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3514454","url":null,"abstract":"Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in wide-area applications. An alternative bi-static backscatter radio architecture, characterized by a dis-aggregated illuminator and receiver, can provide enhanced coverage and, thus, can support wide-area applications. In this paper, we analyze the scalability of the bi-static backscatter radio for large-scale wide-area IoT networks consisting of a large number of unsynchronized, receiver-less tags. We introduce the Tag Drop Rate (TDR) as a measure of reliability and develop a theoretical framework to estimate TDR in terms of the network parameters. We show that under certain approximations, a small-scale prototype can emulate a large-scale network. We then use the measurements from experimental prototypes of bi-static backscatter networks (BNs) to refine the theoretical model. Finally, based on the insights derived from the theoretical model and the experimental measurements, we describe a systematic methodology for tuning the network parameters and identifying the physical layer design requirements for the reliable operation of large-scale bi-static BNs. Our analysis shows that even with a modest physical layer requirement of bit error rate (BER) 0.2, 1000 receiver-less tags can be supported with 99.9% reliability. This demonstrates the feasibility of bi-static BNs for large-scale wide-area IoT applications.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"6-16"},"PeriodicalIF":2.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106216","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}
Pub Date : 2024-12-09DOI: 10.1109/JRFID.2024.3514534
Santosh Nagaraj
This letter aims to significantly improve the communication performance of Radio Frequency Identification (RFID) tags that transmit to Bluetooth devices. Such tags are increasingly being researched in order to provide the benefits of RFID without the need for expensive stand-alone readers. Strictly speaking, these tags do not generate Bluetooth standards-specified waveforms, but are nevertheless compatible with standards-compliant receivers. Gaussian Frequency Shift Keying (GFSK) transmission is replaced by simpler FSK transmission from the tag. This letter analyzes the mismatch effects when FSK signals are demodulated by GFSK receivers. This letter also describes a new tag signaling technique that generates waveforms better matched to Bluetooth receivers than FSK waveforms. The novel technique can be implemented with tag hardware that can generate classical FSK waveforms. Bit error rate improvement of about 5 dB were observed in simulations. Tag coverage area is nearly doubled.
{"title":"On Mitigating the Mismatch Between FSK Tags and GFSK Receivers in BLE Backscatter","authors":"Santosh Nagaraj","doi":"10.1109/JRFID.2024.3514534","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3514534","url":null,"abstract":"This letter aims to significantly improve the communication performance of Radio Frequency Identification (RFID) tags that transmit to Bluetooth devices. Such tags are increasingly being researched in order to provide the benefits of RFID without the need for expensive stand-alone readers. Strictly speaking, these tags do not generate Bluetooth standards-specified waveforms, but are nevertheless compatible with standards-compliant receivers. Gaussian Frequency Shift Keying (GFSK) transmission is replaced by simpler FSK transmission from the tag. This letter analyzes the mismatch effects when FSK signals are demodulated by GFSK receivers. This letter also describes a new tag signaling technique that generates waveforms better matched to Bluetooth receivers than FSK waveforms. The novel technique can be implemented with tag hardware that can generate classical FSK waveforms. Bit error rate improvement of about 5 dB were observed in simulations. Tag coverage area is nearly doubled.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"1-5"},"PeriodicalIF":2.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106217","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}
Pub Date : 2024-12-09DOI: 10.1109/JRFID.2024.3507492
Alessandra Costanzo;Andrea Nardin
{"title":"Guest Editorial of the Special Issue on IEEE WiSEE 2023 Conference","authors":"Alessandra Costanzo;Andrea Nardin","doi":"10.1109/JRFID.2024.3507492","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3507492","url":null,"abstract":"","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"857-859"},"PeriodicalIF":2.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10781442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797931","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}
Pub Date : 2024-11-29DOI: 10.1109/JRFID.2024.3509617
Francesca Maria Chiara Nanni;Gaetano Marrocco
Fingerprinting stands as an effective non-intrusive and non-destructive method to ensure physical security in wireless systems and Radio-Frequency Identification (RFID) applications. Conventionally, the most common state of the art approach involves extracting signal features from the devices and employing machine learning techniques for the classification of counterfeit or cloned ones. This paper explores how to enhance RFID antenna electromagnetic fingerprints by proposing a multi-power interrogation approach. Unlike traditional methods, our technique emphasizes the non-linear behavior of RFID integrated circuits (ICs) by properly varying the reader input power and frequencies. This strategy increases the unpredictability of the IC impedance modulation, thereby extracting richer and more complex information from the RFID tags. Using Shannon Information Theory, we can quantify the entropy of these enhanced fingerprints, revealing an average increase of almost 2 bits in the information content compared to single-power level interrogations. Our findings can lay the foundations to implement more robust RF physical unclonable functions (PUFs) with robust physical keys against counterfeiting and replication threats.
{"title":"Enhancing RFID Antenna Electromagnetic Fingerprints Through Non-Linear Interrogation","authors":"Francesca Maria Chiara Nanni;Gaetano Marrocco","doi":"10.1109/JRFID.2024.3509617","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3509617","url":null,"abstract":"Fingerprinting stands as an effective non-intrusive and non-destructive method to ensure physical security in wireless systems and Radio-Frequency Identification (RFID) applications. Conventionally, the most common state of the art approach involves extracting signal features from the devices and employing machine learning techniques for the classification of counterfeit or cloned ones. This paper explores how to enhance RFID antenna electromagnetic fingerprints by proposing a multi-power interrogation approach. Unlike traditional methods, our technique emphasizes the non-linear behavior of RFID integrated circuits (ICs) by properly varying the reader input power and frequencies. This strategy increases the unpredictability of the IC impedance modulation, thereby extracting richer and more complex information from the RFID tags. Using Shannon Information Theory, we can quantify the entropy of these enhanced fingerprints, revealing an average increase of almost 2 bits in the information content compared to single-power level interrogations. Our findings can lay the foundations to implement more robust RF physical unclonable functions (PUFs) with robust physical keys against counterfeiting and replication threats.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"46-53"},"PeriodicalIF":2.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106212","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}
Pub Date : 2024-11-18DOI: 10.1109/JRFID.2024.3501355
Gene-Jinhan Ng;Muthukannan Murugesh;Eng-Hock Lim;Pei-Song Chee;Jen-Hahn Low;Chun-Hui Tan
A simple, single-layer anti-metal tag that is designed using a truncated patch antenna has been proposed. The patch configuration is simple as it only requires the use of two truncated corners and an inclined I-slit as its effective tuning mechanisms, all of which can be easily made on the single surface of a substrate. With the application of the two tuning mechanisms, the tag resonant frequency can be easily tuned by adjusting the geometrical parameters of the truncations and the I-slit, without the involvement of metallic vias/stubs and multiple-layer structures. It makes such a tag convenient for mass manufacturing. The proposed tag has a miniature size (40 mm $times $ 40 mm $times 1$ .6 mm or $0.1224lambda times 0.1224lambda times 0.0049$ $lambda $ ). It can be effectively read from a distance of $sim ~7$ .5 m (4W Effective isotropic radiated power - EIRP) on metal.
提出了一种使用截尾贴片天线设计的简单的单层反金属标签。贴片结构简单，因为它只需要使用两个截断的角和一个倾斜的i型狭缝作为其有效的调谐机制，所有这些都可以很容易地在衬底的单个表面上完成。利用这两种调谐机构，可以通过调整截断和i型狭缝的几何参数来轻松地调谐标签谐振频率，而无需金属过孔/短节和多层结构的参与。这使得这种标签便于大规模生产。提议的标签具有微型尺寸（40毫米$times $ 40毫米$times 1$ .6毫米或$0.1224lambda times 0.1224lambda times 0.0049$$lambda $）。它可以在距离$sim ~7$ .5 m （4W）的金属上有效读取有效各向同性辐射功率- EIRP。
{"title":"Single-Layer Truncated Patch Antenna With an Inclined I-Slit for Anti-Metal Tag Design","authors":"Gene-Jinhan Ng;Muthukannan Murugesh;Eng-Hock Lim;Pei-Song Chee;Jen-Hahn Low;Chun-Hui Tan","doi":"10.1109/JRFID.2024.3501355","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3501355","url":null,"abstract":"A simple, single-layer anti-metal tag that is designed using a truncated patch antenna has been proposed. The patch configuration is simple as it only requires the use of two truncated corners and an inclined I-slit as its effective tuning mechanisms, all of which can be easily made on the single surface of a substrate. With the application of the two tuning mechanisms, the tag resonant frequency can be easily tuned by adjusting the geometrical parameters of the truncations and the I-slit, without the involvement of metallic vias/stubs and multiple-layer structures. It makes such a tag convenient for mass manufacturing. The proposed tag has a miniature size (40 mm \u0000<inline-formula> <tex-math>$times $ </tex-math></inline-formula>\u0000 40 mm \u0000<inline-formula> <tex-math>$times 1$ </tex-math></inline-formula>\u0000.6 mm or \u0000<inline-formula> <tex-math>$0.1224lambda times 0.1224lambda times 0.0049$ </tex-math></inline-formula>\u0000<inline-formula> <tex-math>$lambda $ </tex-math></inline-formula>\u0000). It can be effectively read from a distance of \u0000<inline-formula> <tex-math>$sim ~7$ </tex-math></inline-formula>\u0000.5 m (4W Effective isotropic radiated power - EIRP) on metal.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"847-856"},"PeriodicalIF":2.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761431","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}
Pub Date : 2024-11-11DOI: 10.1109/JRFID.2024.3486488
Luca Catarinucci;Ultan Mc Carthy;Diego Masotti;Simon Hemour
{"title":"News From CRFID Meetings Guest Editorial of the Special Issue on RFID 2023, SpliTech 2023, and IEEE RFID-TA 2023","authors":"Luca Catarinucci;Ultan Mc Carthy;Diego Masotti;Simon Hemour","doi":"10.1109/JRFID.2024.3486488","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3486488","url":null,"abstract":"","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"831-836"},"PeriodicalIF":2.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10749840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600119","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}
Effective cold chain management is critical across various sectors to ensure the integrity of temperature-sensitive goods, ranging from pharmaceuticals to perishable produce. A key challenge within this domain is maintaining items within their required temperature range, typically between 2°C to 8°C, to prevent spoilage or loss of effectiveness. This paper introduces a cost-effective, integrated solution that combines sensors, controllers, and memory into a compact, power-efficient, and low-cost commercial Bluetooth-based temperature & humidity data logger. The proposed solution is particularly useful not only in safeguarding food and pharmaceuticals but also plays a crucial role in the specific context of vaccine storage, such as those for COVID-19, which demands rigorous temperature adherence to ensure efficacy during storage and transportation. Unlike existing solutions, the proposed solution is equipped with interactive algorithms that monitor and record real-time temperature & humidity data throughout the distribution chain. It features a groundbreaking seamless data logging capability, allowing for wireless data retrieval via Bluetooth-enabled devices such as mobile phones, computers, or laptops. The development and testing of the proposed solution have been conducted in our laboratory, ensuring end-to-end performance and efficiency that meet the stringent standards set by health organizations, including the World Health Organization (WHO). A comprehensive comparative analysis further validates the proposed design’s accuracy, cost-effectiveness, and power efficiency, demonstrating its potential to enhance cold chain management practices universally.
{"title":"IoT-Based Integrated Sensing and Logging Solution for Cold Chain Monitoring Applications","authors":"Lalit Kumar Baghel;Radhika Raina;Suman Kumar;Luca Catarinucci","doi":"10.1109/JRFID.2024.3488534","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3488534","url":null,"abstract":"Effective cold chain management is critical across various sectors to ensure the integrity of temperature-sensitive goods, ranging from pharmaceuticals to perishable produce. A key challenge within this domain is maintaining items within their required temperature range, typically between 2°C to 8°C, to prevent spoilage or loss of effectiveness. This paper introduces a cost-effective, integrated solution that combines sensors, controllers, and memory into a compact, power-efficient, and low-cost commercial Bluetooth-based temperature & humidity data logger. The proposed solution is particularly useful not only in safeguarding food and pharmaceuticals but also plays a crucial role in the specific context of vaccine storage, such as those for COVID-19, which demands rigorous temperature adherence to ensure efficacy during storage and transportation. Unlike existing solutions, the proposed solution is equipped with interactive algorithms that monitor and record real-time temperature & humidity data throughout the distribution chain. It features a groundbreaking seamless data logging capability, allowing for wireless data retrieval via Bluetooth-enabled devices such as mobile phones, computers, or laptops. The development and testing of the proposed solution have been conducted in our laboratory, ensuring end-to-end performance and efficiency that meet the stringent standards set by health organizations, including the World Health Organization (WHO). A comprehensive comparative analysis further validates the proposed design’s accuracy, cost-effectiveness, and power efficiency, demonstrating its potential to enhance cold chain management practices universally.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"837-846"},"PeriodicalIF":2.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645508","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}
Pub Date : 2024-10-28DOI: 10.1109/JRFID.2024.3487303
Stefan Glüge;Matthias Nyfeler;Ahmad Aghaebrahimian;Nicola Ramagnano;Christof Schüpbach
The proliferation of drones, or unmanned aerial vehicles (UAVs), has raised significant safety concerns due to their potential misuse in activities such as espionage, smuggling, and infrastructure disruption. This paper addresses the critical need for effective drone detection and classification systems that operate independently of UAV cooperation. We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components. The focus is on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications. A comprehensive dataset is provided to support future model development. In addition, we demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna, validated through real-world field testing. On our development dataset, all models consistently achieved an average balanced classification accuracy of $ge 85%$ at SNR $gt -12$ dB. In the field test, these models achieved an average balance accuracy of >80%, depending on transmitter distance and antenna direction. Our contributions include: a publicly available dataset for model development, a comparative analysis of CNN for drone detection under low SNR conditions, and the deployment and field evaluation of a practical, low-cost detection system.
{"title":"Robust Low-Cost Drone Detection and Classification Using Convolutional Neural Networks in Low SNR Environments","authors":"Stefan Glüge;Matthias Nyfeler;Ahmad Aghaebrahimian;Nicola Ramagnano;Christof Schüpbach","doi":"10.1109/JRFID.2024.3487303","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3487303","url":null,"abstract":"The proliferation of drones, or unmanned aerial vehicles (UAVs), has raised significant safety concerns due to their potential misuse in activities such as espionage, smuggling, and infrastructure disruption. This paper addresses the critical need for effective drone detection and classification systems that operate independently of UAV cooperation. We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components. The focus is on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications. A comprehensive dataset is provided to support future model development. In addition, we demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna, validated through real-world field testing. On our development dataset, all models consistently achieved an average balanced classification accuracy of \u0000<inline-formula> <tex-math>$ge 85%$ </tex-math></inline-formula>\u0000 at SNR \u0000<inline-formula> <tex-math>$gt -12$ </tex-math></inline-formula>\u0000dB. In the field test, these models achieved an average balance accuracy of >80%, depending on transmitter distance and antenna direction. Our contributions include: a publicly available dataset for model development, a comparative analysis of CNN for drone detection under low SNR conditions, and the deployment and field evaluation of a practical, low-cost detection system.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"821-830"},"PeriodicalIF":2.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595138","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}
Pub Date : 2024-10-18DOI: 10.1109/JRFID.2024.3483197
Barrett D. Durtschi;Andrew M. Chrysler
As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. After the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.
随着射频识别（RFID）方法不断向更高的复杂度发展，一种机器学习的形式正在出现。神经网络（NN）在 RFID 领域的应用正在稳步增加，在定位和活动识别领域，各种研究都取得了可喜的成果。RFID 应用主要分为两类问题，包括回归和分类。我们分析的 RIFD 定位技术属于回归问题，而活动识别属于分类问题。许多作品并没有将自己归类为活动识别方法，但由于它们属于分类范畴，我们仍将其视为活动识别技术。本研究概述了定位领域的神经网络模型，其依据是这些模型是否能独立于测试环境。在活动识别和附件领域，主要方法包括基于标签和无标签方法。在对模型进行调查后，还进行了比较研究，以探讨不同神经网络模型之间提高准确性的原因。
{"title":"Overview of RFID Applications Utilizing Neural Networks","authors":"Barrett D. Durtschi;Andrew M. Chrysler","doi":"10.1109/JRFID.2024.3483197","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3483197","url":null,"abstract":"As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. After the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"801-810"},"PeriodicalIF":2.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595137","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}
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