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27 pages, 1412 KiB  
Article
A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios
by Honggang Wang, Weibing Du, Bo Qin, Ruoyu Pan and Shengli Pang
Electronics 2024, 13(21), 4162; https://doi.org/10.3390/electronics13214162 - 23 Oct 2024
Viewed by 758
Abstract
In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read [...] Read more.
In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios. Full article
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Figure 1
<p>System status evaluation plan.</p>
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<p>Reader identification process, where the red circle indicates the identification range of the reader.</p>
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<p>Reader identification range optimization, where different colors indicate different identification ranges.</p>
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<p>Link sequence diagram.</p>
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<p>Slotted ALOHA algorithm model diagram.</p>
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<p>Reader commands and tag responses.</p>
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<p>Tag identification process.</p>
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<p>MAC layer system status evaluation model.</p>
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<p>RFID system status classification.</p>
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<p>Test scenario. (<b>a</b>) Archive shelf test scenario. (<b>b</b>) Spectrum analyzer test scenario.</p>
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<p>MAC layer system status scoring.</p>
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<p>Link quality evaluation results.</p>
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<p>Classification results of RFID system status, where red markings indicate misclassified samples.</p>
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<p>Comparison of classification algorithm performance.</p>
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29 pages, 14405 KiB  
Article
Data Immunity in Near Field Radio Frequency Communication Systems—NFC as an Aspect of Electromagnetic Information Security
by Andrzej Firlej, Slawomir Musial and Ireneusz Kubiak
Appl. Sci. 2024, 14(13), 5854; https://doi.org/10.3390/app14135854 - 4 Jul 2024
Viewed by 1047
Abstract
The NFC and MIFARE systems (referred to as HF-band RFID) are a special case of Radio Frequency Identification (RFID) technology using a radio frequency of 13.56 MHz for communication. The declared range of such communication is usually several cm and is characterized by [...] Read more.
The NFC and MIFARE systems (referred to as HF-band RFID) are a special case of Radio Frequency Identification (RFID) technology using a radio frequency of 13.56 MHz for communication. The declared range of such communication is usually several cm and is characterized by the need to bring the data carrier close to the system reader. Due to the possibility of transmitting sensitive data in this type of system, an important problem seems to be the electromagnetic security of the transmitted data between the cards (tags) and the reader and within the system. In most of the available research studies, the security of RFID systems comes down to the analysis of the effectiveness of encryption of transmitted data or testing the range of communication between the reader and the identifier. In this research, however, special attention is paid to the so-called electromagnetic information security without the analysis of cryptographic protection. In some cases (e.g., data retransmission), encryption may not be an effective method of securing data (because, e.g., encrypted data might be used to open and start a car with a keyless system). In addition, the research draws attention to the fact that the data from the identifier can be accessed not only from the identifier, but also from the control system (reader, wiring, controller, etc.) from which the data can be radiated (unintentionally) at a much greater distance than the communication range between the identifier and the reader. In order to determine the security of the transmitted data in the HF-band RFID systems, a number of tests were carried out with the use of specialized equipment. During the measurements, both the data carriers themselves (cards, key fobs, stickers, tags) and exemplary systems for reading data from the media (a writable card reader, a mobile phone with NFC function, and an extensive access control system) were tested. The experiments carried out made it possible to determine the safety of NFC and MIFARE systems during their use and only storage (e.g., the ability to read data from an identification card stored in a pocket). Full article
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Figure 1
<p>Sample diagram of an access control system.</p>
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<p>Single-zone controller—one door, entrance and exit.</p>
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<p>Universal reader—LF-band RFID and HF-band RFID.</p>
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<p>Example TAGS identifiers: (<b>a</b>) RFID card, (<b>b</b>) key ring, (<b>c</b>) sticker.</p>
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<p>Structure of data stored in the identifier layout (NTAG213).</p>
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<p>Manchester coding.</p>
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<p>Measuring system for determining the minimum value of the magnetic field strength necessary for the ID to operate.</p>
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<p>Universal LF-band RFID and HF-band RFID ID reader–programmer and frequency characteristics of its radiation.</p>
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<p>Parameters defining the modulation depth factor.</p>
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<p>Examples of NFC signal waveforms corresponding to modulation depth: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>27</mn> <mo>%</mo> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>7</mn> <mo>%</mo> </mrow> </semantics></math>.</p>
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<p>Measuring system for determining the modulation depth factor.</p>
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<p>Recorded signal waveforms corresponding to distance measurements: (<b>a</b>) <math display="inline"><semantics> <mrow> <mn>1.5</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>38</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mn>10</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>14</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>The real circuit for measuring the frequency characteristic of a resonant system.</p>
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<p>The electrical diagram of a circuit for testing the frequency characteristic of a resonant system using the LTspice application.</p>
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<p>Results of tests of the frequency characteristics of the resonant system: (<b>a</b>) in the frequency range of 10 MHz to 20 MHz—simulation in LTSpice application, (<b>b</b>) result of the measurement of the characteristics with a spectrum analyzer in the frequency range from 1 MHz to 40 MHz—actual measurement.</p>
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<p>Measuring system for determining the modulation depth factor using a resonant system.</p>
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<p>Recorded signal waveforms corresponding to distance measurements: (<b>a</b>) <math display="inline"><semantics> <mrow> <mn>3.0</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>27</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mn>15</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mfenced separators="|"> <mrow> <mi>m</mi> <mo>=</mo> <mn>14</mn> <mo>%</mo> </mrow> </mfenced> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <mn>25</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>7</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Recorded signal waveforms corresponding to distance measurements: (<b>a</b>) <math display="inline"><semantics> <mrow> <mn>3.0</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>27</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mn>15</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mfenced separators="|"> <mrow> <mi>m</mi> <mo>=</mo> <mn>14</mn> <mo>%</mo> </mrow> </mfenced> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <mn>25</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>7</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) The ZFL-500LN+ amplifier and (<b>b</b>) its frequency gain characteristic—according to the amplifier manufacturer’s data [<a href="#B21-applsci-14-05854" class="html-bibr">21</a>].</p>
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<p>Measuring system for determining the modulation depth factor using a signal amplifier.</p>
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<p>Recorded signal waveforms corresponding to distance measurements: (<b>a</b>) <math display="inline"><semantics> <mrow> <mn>3.0</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>16</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>, (<b>b</b>) 20 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mfenced separators="|"> <mrow> <mi>m</mi> <mo>=</mo> <mn>13</mn> <mo>%</mo> </mrow> </mfenced> <mo> </mo> </mrow> </semantics></math>(<b>c</b>) <math display="inline"><semantics> <mrow> <mn>25</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>14</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math> and (<b>d</b>) 30 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>7</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Recorded signal waveforms corresponding to distance measurements: (<b>a</b>) <math display="inline"><semantics> <mrow> <mn>3.0</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>16</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>, (<b>b</b>) 20 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mfenced separators="|"> <mrow> <mi>m</mi> <mo>=</mo> <mn>13</mn> <mo>%</mo> </mrow> </mfenced> <mo> </mo> </mrow> </semantics></math>(<b>c</b>) <math display="inline"><semantics> <mrow> <mn>25</mn> <mo> </mo> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>14</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math> and (<b>d</b>) 30 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">m</mi> <mo> </mo> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>7</mn> <mo>%</mo> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Comparison of the measured maximum signal levels (<b>a</b>) and the determined modulation depths (<b>b</b>) using the three methods discussed.</p>
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<p>(<b>a</b>) Diagram of the access control system and (<b>b</b>) its practical implementation.</p>
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<p>An example of a signal waveform recorded from the reader–identification system.</p>
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<p>The time waveform of the signal corresponding to the queries sent by the reader when there is no identifier in range.</p>
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<p>Time waveform of the signal corresponding to the reader’s queries and ID responses.</p>
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<p>Measuring system for testing revealing emissions from the access control system under test.</p>
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<p>Level of electromagnetic disturbances from the tested set of access control.</p>
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<p>Sample images of the obtained waveforms for frequencies equal to (<b>a</b>,<b>b</b>) 13.56 MHz; (<b>c</b>) 27.12 MHz; and (<b>d</b>,<b>e</b>) 40.68 MHz.</p>
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<p>Measuring system for testing revealing emissions from the tested NFC reader–programmer: (<b>a</b>) diagram of the measuring system, (<b>b</b>) photo of the measuring system.</p>
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<p>Level of electromagnetic disturbances from the tested reader/programmer (harmonics observed up to 700 MHz).</p>
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<p>Sample images of the obtained waveforms for frequencies (<b>a</b>,<b>b</b>) 13.56 MHz, (<b>c</b>,<b>d</b>) 27.12 MHz, (<b>e</b>,<b>f</b>) 189.94 MHz, and (<b>g</b>,<b>h</b>) 691.56 MHz.</p>
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<p>Sample images of the obtained waveforms for frequencies (<b>a</b>,<b>b</b>) 13.56 MHz, (<b>c</b>,<b>d</b>) 27.12 MHz, (<b>e</b>,<b>f</b>) 189.94 MHz, and (<b>g</b>,<b>h</b>) 691.56 MHz.</p>
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<p>Measuring system for testing the revealing emissions from the tested mobile phone.</p>
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<p>Level of electromagnetic disturbances from the tested mobile phone.</p>
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<p>Examples of obtained waveforms for a frequency of 13.56 MHz: (<b>a</b>–<b>c</b>).</p>
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20 pages, 2929 KiB  
Article
A High-Precision 3D Target Perception Algorithm Based on a Mobile RFID Reader and Double Tags
by Yaqin Xie, Tianyuan Gu, Di Zheng, Yu Zhang and Hai Huan
Remote Sens. 2023, 15(15), 3914; https://doi.org/10.3390/rs15153914 - 7 Aug 2023
Cited by 1 | Viewed by 1380
Abstract
With the popularization of positioning technology, more and more industries have begun to pay attention to the application and demand of location information, and almost all industries can benefit from low-cost and high-precision location information. This paper introduces a novel three-dimensional (3D) low-cost, [...] Read more.
With the popularization of positioning technology, more and more industries have begun to pay attention to the application and demand of location information, and almost all industries can benefit from low-cost and high-precision location information. This paper introduces a novel three-dimensional (3D) low-cost, high-precision target perception algorithm that utilizes a Radio Frequency Identification (RFID) mobile reader and double tags. Initially, the Received Signal Strength (RSS) is employed to estimate the approximate position of the target along the length direction of the shelf. Additionally, double tags are affixed to the target, enabling the perception of its approximate height and depth through phase information measurements. Subsequently, the obtained rough position serves as an initial value for calibration using the proposed algorithm, allowing for the refinement of the target’s length information relative to the shelf. Simulation results demonstrate the exceptional accuracy of the proposed method in perceiving the 3D position information of the target, achieving centimeter-level sensing accuracy. Full article
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Figure 1
<p>System architecture used by MRRDT.</p>
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<p>System architecture used by MRRDT.</p>
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<p>Changes in RSS measurements when the reader and tag position are fixed.</p>
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<p>The RSS peak time corresponding to different tags.</p>
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<p>Double-tags phase model.</p>
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<p>Diagram of 3D calibration algorithm.</p>
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<p>A two-dimensional plan of the calibration algorithm.</p>
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<p>Calibration algorithm yoz profile.</p>
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<p>Three sets of target positioning errors.</p>
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<p>Three-dimensional results of two methods for locating targets.</p>
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<p>Influence of spacing between tag pairs on positioning accuracy.</p>
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<p>Influence of MRRDT antenna interval on positioning accuracy.</p>
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<p>Influence of MRRDT antenna interval and sampling rate on positioning accuracy.</p>
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<p>Influence of robot moving speed and ambient noise on positioning accuracy.</p>
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22 pages, 690 KiB  
Article
An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
by Jiabin Peng, Lijuan Zhang, Mingqiu Fan, Nan Zhao, Lei Lei, Qirui He and Jiangcheng Xia
Appl. Sci. 2023, 13(4), 2228; https://doi.org/10.3390/app13042228 - 9 Feb 2023
Cited by 3 | Viewed by 1565
Abstract
As one of the key techniques used in the perception layer of the Industrial Internet of Things (IIoT), radio frequency identification (RFID) has been widely applied for object tracing, smart warehouse management, product line monitoring, etc. In most applications, conveyor belts are prevalently [...] Read more.
As one of the key techniques used in the perception layer of the Industrial Internet of Things (IIoT), radio frequency identification (RFID) has been widely applied for object tracing, smart warehouse management, product line monitoring, etc. In most applications, conveyor belts are prevalently implemented to accelerate the sorting efficiency for goods management. However, in such a system, tags quickly go through the reader’s reading range resulting in constant changing of the tag set and limited participating time of moving tags. As a result, it poses more challenges to the tag identification problem in mobile systems than in traditional static applications. In this work, a novel admission-control-based dynamic query tree (ACDQT) protocol is proposed for fast-moving tag identification. In ACDQT, two main strategies are developed, i.e., multi-round admission control (MRAC) and dynamic query tree recognition (DQTR). In MRAC, the reading process of multiple rounds is analyzed, and the number of admitted tags in each round is optimized. Thus, the tag lost ratio is guaranteed, and the identification process can be effectively accelerated. In DQTR, colliding tags are grouped into multiple subsets with the help of consecutive colliding bits in tag responses. By constructing a dynamic query tree, the number of collision slots is greatly reduced, and the identification efficiency in a single round is improved significantly. With MRAC and DQTR, ACDQT can support higher tag flow rate in mobile systems than existing works. Both theoretical analyses and simulation results are presented to demonstrate the effectiveness of ACDQT. Full article
(This article belongs to the Special Issue RFID(Radio Frequency Identification) Localization and Application)
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<p>Conveyor-belt-oriented fast-moving RFID system model.</p>
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<p>Frame slot structure: (<b>a</b>) when <math display="inline"><semantics> <mrow> <mi>f</mi> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, the frame consists of two slots; (<b>b</b>) when <math display="inline"><semantics> <mrow> <mi>f</mi> <mi>l</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, the frame consists of four slots; (<b>c</b>) when <math display="inline"><semantics> <mrow> <mi>f</mi> <mi>l</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, the frame consists of eight slots.</p>
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<p>Multi-round tag recognition for conveyor belt systems.</p>
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<p>Recognition process in a single round.</p>
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<p>Tag’s operation after receiving the reader’s Query command.</p>
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<p>Flowchart of the proposed ACDQT algorithm: (<b>a</b>) the admission control phase with MRAC; (<b>b</b>) the identification phase in each round with DQTR.</p>
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<p>Example of DQTR recognition process.</p>
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<p>Number of slots needed to identify tags: (<b>a</b>) collision slots; (<b>b</b>) idle (or go-on) slots.</p>
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<p>Average number of message bits needed for one tag identification: (<b>a</b>) on the the reader side; (<b>b</b>) on the tag side.</p>
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<p>Time performance: (<b>a</b>) average time for one tag identification; (<b>b</b>) time efficiency.</p>
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<p>Identification performance for fast-moving tags: (<b>a</b>) throughput; (<b>b</b>) tag lost ratio.</p>
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20 pages, 6711 KiB  
Article
DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things
by Rachid Mafamane, Mourad Ouadou, Hajar Sahbani, Nisrine Ibadah and Khalid Minaoui
Computers 2022, 11(7), 107; https://doi.org/10.3390/computers11070107 - 30 Jun 2022
Cited by 6 | Viewed by 2679
Abstract
Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and [...] Read more.
Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and reduces the performance of the RFID system. Therefore, communication resource management is required to avoid such problems. In this paper, we propose an anti-collision protocol based on feed-forward Artificial Neural Network methodology for distributed learning between RFID readers to predict collisions and ensure efficient resource allocation (DMLAR) by considering the mobility of tags and readers. The evaluation of our anti-collision protocol is performed for different mobility scenarios in healthcare where the collected data are critical and must respect the terms of throughput, delay, overload, integrity and energy. The dataset created and distributed by the readers allows an efficient learning process and, therefore, a high collision detection to increase throughput and minimize data loss. In the application phase, the readers do not need to exchange control packets with each other to control the resource allocation, which avoids network overload and communication delay. Simulation results show the robustness and effectiveness of the anti-collision protocol by the number of readers and resources used. The model used allows a large number of readers to use the most suitable frequency and time resources for simultaneous and successful tag interrogation. Full article
(This article belongs to the Special Issue Edge Computing for the IoT)
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Graphical abstract

Graphical abstract
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<p>RFID system communication process.</p>
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<p>RFID collisions. (<b>a</b>) Reader-to-Reader Interference; (<b>b</b>) Reader-to-Tag Interference.</p>
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<p>RFID protocol classification.</p>
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<p>Proposed algorithm scheme.</p>
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<p>Artificial Neural Network architecture for RFID readers.</p>
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<p>A scenario of a mobile reader in a RFID network.</p>
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<p>RFID reader network for directed mobility.</p>
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<p>Best validation performance for RRI model.</p>
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<p>Best validation performance for RTI model.</p>
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<p>Performance of RRI collision prediction methods.</p>
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<p>Performance of RTI collision prediction methods.</p>
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<p>Collision prediction vs. number of used resources (frequency and time slots) for the directed mobility model.</p>
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<p>Collision prediction vs. number time slots (50 readers, 10 frequencies).</p>
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<p>Collision prediction vs. number of frequencies (50 readers, 10 time slots).</p>
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<p>Performance vs. number of readers.</p>
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<p>Failed interrogations vs. number of readers.</p>
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<p>Network overload vs. number of readers.</p>
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<p>Total interrogation time vs. number of readers.</p>
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<p>Consuming energy vs. number of readers.</p>
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12 pages, 1609 KiB  
Article
Mobile Robots and RFID Technology-Based Smart Care Environment for Minimizing Risks Related to Employee Turnover during Pandemics
by Anja Poberznik, Mirka Leino, Jenni Huhtasalo, Taina Jyräkoski, Pauli Valo, Tommi Lehtinen, Joonas Kortelainen, Sari Merilampi and Johanna Virkki
Sustainability 2021, 13(22), 12809; https://doi.org/10.3390/su132212809 - 19 Nov 2021
Cited by 7 | Viewed by 2789
Abstract
During a pandemic, it is imperative that all staff members have up-to-date information on changing work practices in the healthcare environment. This article presents a way to implement work environment orientation amongst different groups in care facilities by utilizing mobile robots, radio frequency [...] Read more.
During a pandemic, it is imperative that all staff members have up-to-date information on changing work practices in the healthcare environment. This article presents a way to implement work environment orientation amongst different groups in care facilities by utilizing mobile robots, radio frequency identification (RFID) technologies, and data synthesis. We offer a scenario based on a co-design approach, in which a mobile robot works as an orientation guide for new employees, RFID tags are applied on objects around the premises and people’s clothing. The mobile robot takes advantage of the information provided by its known location and each RFID tag read by the RFID reader integrated with the robot. We introduce the scenario here, along with the details of its practical test implementation. Further, the challenges met in the test implementation are discussed as well as the future potential of its application. In conclusion, our study indicates that repetitive training and orientation-related duties can be successfully transferred to a mobile robot. Through RFID, the mobile robot can deliver the relevant information to the right people and thus contribute to patient and personnel safety and the resource efficiency of the orientation process. Full article
(This article belongs to the Special Issue Role of Smart eHealth and eCare Technologies in the Age of Pandemics)
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<p>The technical setup of the mobile robot and RFID technology-based orientation system.</p>
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<p>In the orientation work, the mobile robot meets and identifies the caregiver based on the RFID tags on her shirt.</p>
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<p>A flow chart of the basic principle of the system.</p>
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<p>The caregiver’s personal RFID tags are on the front and back of the shirt.</p>
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13 pages, 3120 KiB  
Article
Reader–Tag Commands via Modulation Cutoff Intervals in RFID Systems
by Abdallah Y. Alma’aitah and Mohammad A. Massad
Future Internet 2021, 13(9), 235; https://doi.org/10.3390/fi13090235 - 16 Sep 2021
Cited by 1 | Viewed by 1966
Abstract
Radio frequency identification (RFID) technology facilitates a myriad of applications. In such applications, an efficient reader–tag interrogation process is crucial. Nevertheless, throughout reader–tag communication, significant amounts of time and power are consumed on inescapable simultaneous tag replies (i.e., collisions) due to the lack [...] Read more.
Radio frequency identification (RFID) technology facilitates a myriad of applications. In such applications, an efficient reader–tag interrogation process is crucial. Nevertheless, throughout reader–tag communication, significant amounts of time and power are consumed on inescapable simultaneous tag replies (i.e., collisions) due to the lack of carrier sensing at the tags. This paper proposes the modulation cutoff intervals (MCI) process as a novel reader–tag interaction given the lack of carrier sensing constraints in passive RFID tags. MCI is facilitated through a simple digital baseband modulation termination (DBMT) circuit at the tag. DBMT detects the continuous-wave cutoff by the reader. In addition, DBMT provides different flags based on the duration of the continuous-wave cutoff. Given this capability at the tag, the reader cuts off its continuous-wave transmission for predefined intervals to indicate different commands to the interrogated tag(s). The MCI process is applied to tag interrogation (or anti-collision) and tag-counting protocols. The MCI process effect was evaluated by the two protocols under high and low tag populations. The performance of such protocols was significantly enhanced with precise synchronization within time slots with more than 50% and more than 55.6% enhancement on time and power performance of anti-collision and counting protocols, respectively. Through the MCI process, fast and power-efficient tag identification is achieved in inventory systems with low and high tag mobility; alternatively, in addition to the rapid and power efficient interaction with tags, anonymous tag counting is conducted by the proposed process. Full article
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<p>CWAD circuit after the rectifier in the general components of a passive RFID tag [<a href="#B10-futureinternet-13-00235" class="html-bibr">10</a>].</p>
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<p>Different CW cutoff detection times between two tags due to the difference in the initial rectifier output voltage, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">V</mi> <mi mathvariant="normal">H</mi> </msub> </mrow> </semantics></math>.</p>
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<p>CWAD circuit consisting mostly of passive components [<a href="#B10-futureinternet-13-00235" class="html-bibr">10</a>].</p>
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<p>CWAD DBMT circuit location within typical passive RFID components.</p>
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<p>DBMT module and its placement after the ED.</p>
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<p>Timing diagram example of the DBMT outputs for a tag sending PIE encoded stream of bits starting by 01100··· and CW cutoff by the reader at the 4th bit.</p>
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<p>MCI process in anti-collision protocols with PIE encoding.</p>
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<p>Three different scenarios of reader commands in anti-collision protocols with MCI process.</p>
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<p>MCI process in anti-collision protocols with PIE encoding to accommodate MCI commands and other commands that are sent explicitly not through the MCI.</p>
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<p>Total number of collision slots vs. different tag populations (from 100 to 4000 tags).</p>
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<p>Average identification time for different tag population in the standardized Q-Algorithm.</p>
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<p>Average identification time for different unknown tag populations in the deterministic tree-based algorithm.</p>
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<p>Comparison of average counting/estimation time of tags under different protocols.</p>
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17 pages, 4149 KiB  
Article
Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems
by Elias Hatem, Sergio Fortes, Elizabeth Colin, Sara Abou-Chakra, Jean-Marc Laheurte and Bachar El-Hassan
Sensors 2021, 21(16), 5346; https://doi.org/10.3390/s21165346 - 8 Aug 2021
Cited by 8 | Viewed by 3492
Abstract
Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization [...] Read more.
Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity. Full article
(This article belongs to the Collection Intelligent Wireless Networks)
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<p>The Pioneer 3-DX mobile robot.</p>
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<p>The diagram of the straight line test.</p>
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<p>Clockwise and counterclockwise rotation error.</p>
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<p>The diagram of a square path clockwise and counterclockwise.</p>
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<p>(<b>a</b>) Coin ID RFID tag and (<b>b</b>) UTP Diff 2 RFID reader.</p>
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<p>Block diagram of the offline and online stages.</p>
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<p>Experimental environment view.</p>
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<p>Auto-fingerprint map.</p>
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<p>Two-dimensional configuration of the eight random positions.</p>
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<p>Multilateration technique using four independent tags.</p>
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<p>CDF for the position errors using the auto-fingerprinting technique.</p>
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20 pages, 7338 KiB  
Article
An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios
by Andrea Motroni, Alice Buffi, Paolo Nepa, Mario Pesi and Antonio Congi
Sensors 2021, 21(15), 5183; https://doi.org/10.3390/s21155183 - 30 Jul 2021
Cited by 25 | Viewed by 3835
Abstract
The I-READ 4.0 project is aimed at developing an integrated and autonomous Cyber-Physical System for automatic management of very large warehouses with a high-stock rotation index. Thanks to a network of Radio Frequency Identification (RFID) readers operating in the Ultra-High-Frequency (UHF) band, both [...] Read more.
The I-READ 4.0 project is aimed at developing an integrated and autonomous Cyber-Physical System for automatic management of very large warehouses with a high-stock rotation index. Thanks to a network of Radio Frequency Identification (RFID) readers operating in the Ultra-High-Frequency (UHF) band, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in an indoor scenario. A key component to accomplish this goal is the UHF-RFID Smart Gate, which consists of a checkpoint infrastructure based on RFID technology to identify forklifts and their direction of transit. This paper presents the implementation of a UHF-RFID Smart Gate with a single reader antenna with asymmetrical deployment, thus allowing the correct action classification with reduced infrastructure complexity and cost. The action classification method exploits the signal phase backscattered by RFID tags placed on the forklifts. The performance and the method capabilities are demonstrated through an on-site demonstrator in a real warehouse. Full article
(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)
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<p><span class="html-italic">Tassignano</span> warehouse plan.</p>
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<p>The I-READ 4.0 framework.</p>
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<p>(<b>a</b>) Column composed by two tagged pallets and (<b>b</b>) sketch of the tagged label applied on the pallet (the tag is on the label rear side).</p>
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<p>RFID label printer at the end of the production line.</p>
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<p>RFID Smart Gates installed at two entrances of the <span class="html-italic">Tassignano</span> warehouse.</p>
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<p>RFID tags placed on the forklift. (<b>a</b>) Tag placed on the upright, and (<b>b</b>) tag placed on the forklift top.</p>
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<p>Photocells installed at one of the UHF-RFID Smart Gates.</p>
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<p>Sketch of the symmetrical configuration of the RFID Smart Gate.</p>
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<p>Time behavior of the unwrapped normalized phase in the symmetrical configuration of the RFID Smart Gate for the following system parameters: <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mn>865.7</mn> </mrow> </semantics></math> MHz, <math display="inline"><semantics> <mrow> <mi>l</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> m, <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> m/s, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>h</mi> <mo>=</mo> <mn>2.5</mn> </mrow> </semantics></math> m, and <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> ms.</p>
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<p>Sketch of the asymmetrical gate.</p>
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<p>Time behavior of the normalized unwrapped phase in the asymmetrical antenna deployment for <span class="html-italic">IN</span> and <span class="html-italic">OUT</span> actions by varying the speed <span class="html-italic">v</span> when the parameters are the following: <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <mn>865.7</mn> </mrow> </semantics></math> MHz, <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> m, <math display="inline"><semantics> <mrow> <msub> <mi>l</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> m, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>h</mi> <mo>=</mo> <mn>2.5</mn> </mrow> </semantics></math> m, and <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> ms.</p>
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<p>Examples of measured normalized unwrapped phase <math display="inline"><semantics> <mrow> <msup> <mi>ϕ</mi> <mi>n</mi> </msup> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>. (<b>a</b>) Successful <span class="html-italic">IN</span> classification, (<b>b</b>) wrong <span class="html-italic">IN</span> classification, (<b>c</b>) successful <span class="html-italic">OUT</span> classification, (<b>d</b>) wrong <span class="html-italic">OUT</span> classification.</p>
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<p>Classification accuracy vs. forklift speed <span class="html-italic">v</span>.</p>
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<p>Average number of samples vs. forklift speed <span class="html-italic">v</span>.</p>
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<p>Histogram of the processing time (ms) for the <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> <mo>=</mo> <mn>264</mn> </mrow> </semantics></math> analyzed trials.</p>
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36 pages, 22845 KiB  
Article
A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
by Chin-Ling Chen, Zi-Yi Lim, Hsien-Chou Liao, Yong-Yuan Deng and Peizhi Chen
Appl. Sci. 2021, 11(11), 4939; https://doi.org/10.3390/app11114939 - 27 May 2021
Cited by 13 | Viewed by 4324
Abstract
Tobacco products are an addictive commodity. According to the World Health Organization’s (WHO) latest statistics data, tobacco kills more than eight million people each year. In 2003, the WHO proposed the Framework Convention on Tobacco Control (FCTC) to provide an effective framework for [...] Read more.
Tobacco products are an addictive commodity. According to the World Health Organization’s (WHO) latest statistics data, tobacco kills more than eight million people each year. In 2003, the WHO proposed the Framework Convention on Tobacco Control (FCTC) to provide an effective framework for the control of tobacco products to governments around the world. In the field of tobacco products, the hardest problem is how to prevent counterfeit tobacco products and smuggling. To solve the problems, we proposed a blockchain-based traceable and verifiable logistics system for tobacco products with global positioning system (GPS) and radio-frequency identification (RFID) Technologies. In this research, we provide an overview of system architecture, and also define the protocol and the smart contract in every phase that stores data into the blockchain center. We realized a decentralized database and authentication system that uses blockchain and smart contract technology; every protocol in every phase was designed to achieve the integrity of data and non-repudiation of message. Every tobacco product’s shipping record will be completed by scanning the RFID tag and retrieving the GPS with a mobile reader, where the record will be updated and validated in the blockchain center. In the end, the security and costs of the system were analyzed, and a comparison was made with the EU’s (European Commission) method. Our system is more flexible for transportation, more secure in the communication protocol, and more difficult to tamper and forge data. In general, the proposed scheme solved the problem of tobacco products counterfeiting and tracking issues. Full article
(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)
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<p>Hyperledger Fabric blockchain network [<a href="#B28-applsci-11-04939" class="html-bibr">28</a>].</p>
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<p>System architecture diagram.</p>
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<p>Blockchain center network architecture.</p>
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<p>Chaincode structure of the access party and the enumeration of the role type.</p>
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<p>The chaincode structure of the tobacco products.</p>
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<p>The flowchart of the registration phase.</p>
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<p>The flowchart of the authentication phase.</p>
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<p>The flowchart of the issuing ID and manufacture phase.</p>
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<p>The flowchart of the logistics phase from shipper to logistics.</p>
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<p>The flowchart of the logistics phase from logistics to the recipient.</p>
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<p>The flowchart of the consumption phase.</p>
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<p>Consumer checking tobacco in the verification phase.</p>
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<p>The audit mechanism in the verification phase.</p>
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<p>The validation flow in the arbitration phase.</p>
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<p>The mechanism of the blockchain architecture.</p>
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13 pages, 316 KiB  
Article
Privacy-Preserving RFID-Based Search System
by Ji Young Chun and Geontae Noh
Electronics 2021, 10(5), 599; https://doi.org/10.3390/electronics10050599 - 4 Mar 2021
Cited by 1 | Viewed by 1745
Abstract
The employment of mobile readers (or mobile phone collaborated with a Radio frequency identification (RFID) reader) opens a novel application for RFID technology. In particular, an RFID tag search system has been designed to find a particular tag in a group of tags [...] Read more.
The employment of mobile readers (or mobile phone collaborated with a Radio frequency identification (RFID) reader) opens a novel application for RFID technology. In particular, an RFID tag search system has been designed to find a particular tag in a group of tags using a mobile reader. Unfortunately, privacy infringement and availability issues in the search system have not been adequately addressed to date. In this paper, we propose a novel RFID tag search protocol that will enhance mobile reader user privacy while being able to operate under conditions of unstable connection to a central server. First, the proposed protocol preserves the privacy of mobile reader users. The privacy of the mobile reader user is at risk because the signal strength emitted from a mobile reader is much stronger than that from the tag, exposing the location of the mobile reader user and thus compromising the user’s privacy. Thus far, such privacy issues have been overlooked. The second issue is presented because of wireless connections that are either unreliable or too remote, causing a mobile reader to disconnect from the central server. The proposed protocol enables serverless RFID tag searches with passive tags, which obtain operating power from the mobile reader. In unstable environments, the protocol can successfully locate specific tags without any server. Full article
(This article belongs to the Special Issue Advanced RFID Technology and Applications)
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<p>Our Overall Protocol.</p>
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<p>Tag Search Protocol.</p>
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<p>Access List Update Protocol.</p>
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15 pages, 639 KiB  
Article
5G-Compliant Authentication Protocol for RFID
by Jorge Munilla, Adel Hassan and Mike Burmester
Electronics 2020, 9(11), 1951; https://doi.org/10.3390/electronics9111951 - 19 Nov 2020
Cited by 8 | Viewed by 3277
Abstract
The term “Internet of Things” was originally coined when radio frequency identification (RFID) technology was being developed to refer to applications where RFID tagged objects and sensors enabled computers to achieve effective situational awareness without human intervention. Currently, this term encompasses a myriad [...] Read more.
The term “Internet of Things” was originally coined when radio frequency identification (RFID) technology was being developed to refer to applications where RFID tagged objects and sensors enabled computers to achieve effective situational awareness without human intervention. Currently, this term encompasses a myriad of medium/small devices connected to the Internet. On the other hand, 5G is a key enabling technology that will support next generation wireless communications. Moreover, 5G aims to realize the “Internet of Everything”. Surprisingly, despite the expected relationship between these two technologies, RFID tags have not been properly integrated into 4G and it is not clear if this will change in 5G. RFID is considered as a parallel technology where, at best, it has connection to the core network using back-end servers as gateways between the two technologies. With the aim of overcoming this problem, this paper proposes a 5G compliant RFID protocol that allows RFID tags to act as fully fledged 5G subscribers while taking into account the main characteristics of RFID systems. This proposal leverages the separation between USIM and mobile equipment within the user equipment to implement a 5G compliant protocol where tags accomplish the authentication part, as 5G subscribers, while readers assume the mobile equipment role, carrying out the 5G communication and most of the resource consuming tasks. Full article
(This article belongs to the Special Issue Advanced RFID Technology and Applications)
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<p>Here: 5G architecture and the main elements involved in the security procedures.</p>
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<p>5G-authentication and key agreement (AKA).</p>
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<p>Equivalent Architecture for the 5G-compliant radio frequency identification (RFID) protocol.</p>
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<p>5G-compliant RFID Initialization Protocol.</p>
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<p>5G-compliant RFID Authentication Protocol.</p>
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19 pages, 697 KiB  
Article
RF-SML: A SAR-Based Multi-Granular and Real-Time Localization Method for RFID Tags
by Yue Jiang, Yongtao Ma, Hankai Liu and Yunlei Zhang
Electronics 2020, 9(9), 1447; https://doi.org/10.3390/electronics9091447 - 4 Sep 2020
Cited by 8 | Viewed by 2735
Abstract
With the rapid development of the Internet of Things (IoT) technology, location based service in context awareness has received increasing attention. As one of the main localization technologies, UHF RFID technology has been widely used in many fields of life and industry due [...] Read more.
With the rapid development of the Internet of Things (IoT) technology, location based service in context awareness has received increasing attention. As one of the main localization technologies, UHF RFID technology has been widely used in many fields of life and industry due to its advantages. In this article, we introduce a RFID-based system RF-SML, which is a method for quickly and accurately locating static objects via the tag and mobile reader. Specifically, the method utilizes the idea of multi-granularity in order to find the high-probability region of the target position by reconstructing the reflection coefficient of the scene in the coarse-grained localization stage. Subsequently, in the fine-grained localization stage, the grid is traversed in this area to calculate the corresponding evaluation factor to determine the final position result, thereby reducing the time-consuming of localization calculation. At the same time, it uses phase calibration to remove the phase offsets that are caused by the hardware device and the antenna phase center, thereby obtaining higher localization accuracy. We conduct experiments to verify the performance of RF-SML with commercial-off-the-shelf (COTS) RFID equipment. The results show that the proposed method can efficiently achieve the centimeter-level positioning of objects. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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<p>The flow chart for RF-SML system. The algorithm consists of three parts: the data preparation, the coarse-grained localization, and the fine-grained localization.</p>
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<p>Radio frequency identification (RFID) signal propagation model.</p>
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<p>RF-SML localization model (the reader antenna moves along the x-coordinate axis).</p>
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<p>Localization results of the RF-SML algorithm. (<b>a</b>) The coarse-grained localization. (<b>b</b>) The fine-grained localization without phase correction. (<b>c</b>) The fine-grained localization with phase correction. The black outline indicates the high probability region of the tag. The white asterisk and the yellow cross represent the estimated position and the ground truth, respectively.</p>
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<p>The wrapped phase curve.</p>
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<p>The unwrapped phase curve.</p>
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<p>Antenna phase center offset related to AOA.</p>
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<p>Experimental environment and equipment.</p>
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<p>Localization accuracy error comparison with regard to the frequency. The bars represent the average localization accuracy errors in x-axis, y-axis and combined dimension, respectively. Additionally, the whiskers represent the standard deviations of the corresponding localization accuracy errors.</p>
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<p>Localization accuracy error comparison with regard to the sampling interval. The bars represent the average localization accuracy errors in x-axis, y-axis and combined dimension, respectively. And the whiskers represent the standard deviations of the corresponding localization accuracy errors.</p>
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<p>Localization accuracy error comparison with regard to the aperture distance. The bars represent the average localization accuracy errors in x-axis, y-axis and combined dimension, respectively. Additionally, the whiskers represent the standard deviations of the corresponding localization accuracy errors.</p>
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<p>Cumulative distribution function (CDF) of RF-SML localization accuracy.</p>
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<p>Localization accuracy error comparison among different methods. The bars represent the average localization accuracy errors in x-axis, y-axis, and combined dimension, respectively. Additionally, the whiskers represent the standard deviations of the corresponding localization accuracy errors.</p>
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<p>Computational delay comparison among different methods</p>
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21 pages, 6399 KiB  
Article
A Mobility Aware Binary Tree Algorithm to Resolve RFID Jam and Bottleneck Problems in a Next Generation Specimen Management System
by Yen-Hung Chen, Yen-An Chen and Shu-Rong Huang
Micromachines 2020, 11(8), 755; https://doi.org/10.3390/mi11080755 - 4 Aug 2020
Cited by 3 | Viewed by 2751
Abstract
Hospitals are continuously working to reduce delayed analysis and specimen errors during transfers from testing stations to clinical laboratories. Radio-frequency identification (RFID) tags, which provide automated specimen labeling and tracking, have been proposed as a solution to specimen management that reduces human resource [...] Read more.
Hospitals are continuously working to reduce delayed analysis and specimen errors during transfers from testing stations to clinical laboratories. Radio-frequency identification (RFID) tags, which provide automated specimen labeling and tracking, have been proposed as a solution to specimen management that reduces human resource costs and analytic delays. Conventional RFID solutions, however, confront the problem of traffic jams and bottlenecks on the conveyor belts that connect testing stations with clinical laboratories. This mainly results from methods which assume that the arrival rate of specimens to laboratory RFID readers is fixed/stable, which is unsuitable and impractical in the real world. Previous RFID algorithms have attempted to minimize the time required for tag identification without taking the dynamic arrival rates of specimens into account. Therefore, we propose a novel RFID anti-collision algorithm called the Mobility Aware Binary Tree Algorithm (MABT), which can be used to improve the identification of dynamic tags within the reader’s coverage area and limited dwell time. Full article
(This article belongs to the Special Issue Next Generation RFID Transponders)
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<p>Relationship between frame and time slot.</p>
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<p>Collisions in a radio-frequency identification (RFID) system.</p>
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<p>Dynamic RFID system.</p>
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<p>Examples of dynamic tag models.</p>
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<p>Schedule-based anti-collision protocol (SAC) algorithm setup.</p>
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<p>Concept of mobility aware binary tree algorithm (MABT): (<b>a</b>) MABT identification algorithm design; and (<b>b</b>) relationship between super frame and time slot.</p>
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<p>Flowchart of MABT.</p>
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<p>Code for group reader.</p>
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<p>Code for identification reader.</p>
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<p>Tag code operation.</p>
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<p>MABT example: (<b>a</b>) frame <span class="html-italic">i</span>; (<b>b</b>) parameters of frame <span class="html-italic">i</span>; (<b>c</b>) frame <span class="html-italic">i</span> + 1; and (<b>d</b>) parameters of frame <span class="html-italic">i</span> + 1.</p>
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<p>MABT example: (<b>a</b>) frame <span class="html-italic">i</span>; (<b>b</b>) parameters of frame <span class="html-italic">i</span>; (<b>c</b>) frame <span class="html-italic">i</span> + 1; and (<b>d</b>) parameters of frame <span class="html-italic">i</span> + 1.</p>
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<p>Simulated environment.</p>
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<p>Average distance between tags: (<b>a</b>) fixed distance with one tag/column; (<b>b</b>) variable distance with one tag/column; (<b>c</b>) fixed distance with two tags/column; and (<b>d</b>) variable distance with two tags/column.</p>
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<p>Average distance between tags: (<b>a</b>) fixed distance with one tag/column; (<b>b</b>) variable distance with one tag/column; (<b>c</b>) fixed distance with two tags/column; and (<b>d</b>) variable distance with two tags/column.</p>
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<p>Average tag number per column: (<b>a</b>) fixed <span class="html-italic">n</span> tags/line; and (<b>b</b>) average <span class="html-italic">n</span> tags/line.</p>
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<p>Total number of time slots used at different rates.</p>
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<p>Identification rate of different velocities at different breakpoints.</p>
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19 pages, 1252 KiB  
Article
MRLIHT: Mobile RFID-Based Localization for Indoor Human Tracking
by Qian Ma, Xia Li, Guanyu Li, Bo Ning, Mei Bai and Xite Wang
Sensors 2020, 20(6), 1711; https://doi.org/10.3390/s20061711 - 19 Mar 2020
Cited by 9 | Viewed by 3462
Abstract
Radio Frequency Identification (RFID) technology has been widely used in indoor location tracking, especially serving human beings, due to its advantage of low cost, non-contact communication, resistance to hostile environments and so forth. Over the years, many indoor location tracking methods have been [...] Read more.
Radio Frequency Identification (RFID) technology has been widely used in indoor location tracking, especially serving human beings, due to its advantage of low cost, non-contact communication, resistance to hostile environments and so forth. Over the years, many indoor location tracking methods have been proposed. However, tracking mobile RFID readers in real-time has been a daunting task, especially for achieving high localization accuracy. In this paper, we propose a new Mobile RFID (M-RFID)-based Localization approach for Indoor Human Tracking, named MRLIHT. Based on the M-RFID model where RFID readers are equipped on the moving objects (human beings) and RFID tags are fixed deployed in the monitoring area, MRLIHT implements the real-time indoor location tracking effectively and economically. First, based on the readings of multiple tags detected by an RFID reader simultaneously, MRLIHT generates the response regions of tags to the reader. Next, MRLIHT determines the potential location region of the reader where two algorithms are devised. Finally, MRLIHT estimates the location of the reader by dividing the potential location region of the reader into finer-grained grids. The experimental results demonstrate that the proposed MRLIHT performs well in both accuracy and scalability. Full article
(This article belongs to the Section Internet of Things)
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Figure 1
<p>The plan of a large-scale indoor exhibition area.</p>
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<p>(<b>a</b>) The response region of tag <math display="inline"><semantics> <msub> <mi>T</mi> <mn>4</mn> </msub> </semantics></math> to reader <math display="inline"><semantics> <msub> <mi>R</mi> <mn>3</mn> </msub> </semantics></math> and (<b>b</b>) the Potential Location Region (PLR) of reader <math display="inline"><semantics> <msub> <mi>R</mi> <mn>3</mn> </msub> </semantics></math>.</p>
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<p>The structure of the inverted index for Radio Frequency Identification (RFID) tags.</p>
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<p>(<b>a</b>) Distribution of tags detected by <math display="inline"><semantics> <msub> <mi>R</mi> <mn>3</mn> </msub> </semantics></math> and (<b>b</b>) readings of tags by reader <math display="inline"><semantics> <msub> <mi>R</mi> <mn>3</mn> </msub> </semantics></math>.</p>
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<p>Varying <math display="inline"><semantics> <msub> <mi>δ</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>δ</mi> <mi>s</mi> </msub> </semantics></math> with 50, 100, 200, 500 tags can be detected by a reader simultaneously.</p>
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<p>Varying <math display="inline"><semantics> <mi>σ</mi> </semantics></math> with <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> cm, <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> <mo>_</mo> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> cm.</p>
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<p>Varying grid size with <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> cm, <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p>
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<p>Varying the number of tags detected simultaneously with <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> cm, <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> <mo>_</mo> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> </mrow> </semantics></math> = 60 cm.</p>
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<p>Varying the number of readers with <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>d</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> cm, <math display="inline"><semantics> <mrow> <msub> <mi>δ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> <mo>_</mo> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> cm.</p>
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