Capture-Aware Identification of Mobile RFID Tags With Unreliable Channels

Su, Jian; Sheng, Zhengguo; Liu, Alex X.; Yu, Han; Chen, Yongrui

doi:10.1109/tmc.2020.3024076

Cited by 34 publications

(32 citation statements)

References 49 publications

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“…is paper evaluates the performance of our proposed EFB-ACA algorithm, SUBF-DFSA, ERE-ABS, DS-MAP, and LC-DFSA in the error-prone channel. To maintain the convergence, Monte Carlo method is used in the simulations [18][19][20][21]. Figure 1 compares the convergence phase duration of various algorithms in error-prone channel.…”

Section: Experimental Studymentioning

confidence: 99%

An Efficient Early Frame Breaking Strategy for RFID Tag Identification in Large-Scale Industrial Internet of Things

Luo

2021

Scientific Programming

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With the increase in the number of tags, an efficient approach of tag identification is becoming an urgent need in Industrial Internet of Things (IIoT). However, the identification performance of existing Aloha-based anticollision schemes is limited when the initial frame size is seriously mismatched with the actual tag population size. The performance will degrade further when IIoT is deployed in the error-prone channel environment. To optimize the identification performance of RFID system in an error-prone channel environment, we propose an efficient early frame breaking strategy based anticollision algorithm (EFB-ACA) with channel awareness. The EFB-ACA divides the whole tag identification process into two phases: convergence phase and identification phase. The function of convergence phase is to make the adjusted frame quickly converge to an appropriate size. The early frame breaking strategy is embedded in the convergence phase. Numerical results show that the proposed EFB-ACA algorithm outperforms the other methods on efficiency and stability in the error-prone channel. In addition, EFB-ACA algorithm also outperforms the other methods in the error-free channel.

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Section: Experimental Studymentioning

confidence: 99%

An Efficient Early Frame Breaking Strategy for RFID Tag Identification in Large-Scale Industrial Internet of Things

Luo

2021

Scientific Programming

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“…There is also great progress in remote sensing positioning. In 2020, it has been proposed that Capture-aware identification of mobile RFID tags with unreliable channels [31]；a partitioning approach to RFID identification [32] ； a time and energy saving based frame adjustment strategy (TES-FAS) tag identification algorithm for UHF RFID systems [33]；Idle slots skipped mechanism based tag identification algorithm with enhanced collision detection [34]；Redundant rule detection for software-defined networking [35].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Model of VGG-16 for Remote Sensing Image Classification

Chang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In planetary science, it is an important basic work to recognize and classify the features of topography and geomorphology from the massive data of planetary remote sensing.Therefore, this paper proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model.According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85% to 98% now. At the same time, the model has a great improvement in convergence speed and classification performance.By inputting the remote sensing image data of ultralow pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points.Therefore, the model has a good application prospect in remote sensing image fine classification, very low pixel, less image classification.

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“…Research related to emergencies involves public opinion [1][2][3], network security [4][5][6], and data mining analysis [7][8][9][10][11][12]. Ontology models have been widely used in the fields of knowledge engineering and natural language processing.…”

Section: Introductionmentioning

confidence: 99%

EOM-NPOSESs: Emergency Ontology Model Based on Network Public Opinion Spread Elements

Dong

Zhang

Tan

et al. 2021

Security and Communication Networks

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The construction of an emergency ontology model plays an important role in emergency management, which is an important basis for emergency public opinion management and decision-making. Integration of network public opinion spread elements into the emergency ontology model is crucial for realizing knowledge sharing in the field of emergency and public opinion responses. In this study, we crawl a large amount of emergency data from different data sources and construct an emergency dataset. Based on this dataset, we analyze the public opinion elements of emergencies and propose an emergency ontology model based on network public opinion spread elements (EOM-NPOSESs). Thereafter, we consider the coronavirus disease (COVID-19) emergency as an example to construct the EOM-NPOSESs. Finally, we design some strategies to realize rule reasoning and present the COVID-19 emergency application based on the constructed EOM-NPOSESs and the geographic information system platform. The results demonstrate that EOM-NPOSESs can not only describe the semantic relationship between emergencies and emergency elements but also perform semantic logical reasoning on different emergencies.

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Capture-Aware Identification of Mobile RFID Tags With Unreliable Channels

Abstract: Yongrui (2020) Capture-aware identification of mobile RFID tags with unreliable channels. IEEE Transactions on Mobile Computing.

Cited by 34 publications

References 49 publications

An Efficient Early Frame Breaking Strategy for RFID Tag Identification in Large-Scale Industrial Internet of Things

An Efficient Early Frame Breaking Strategy for RFID Tag Identification in Large-Scale Industrial Internet of Things

A Lightweight Model of VGG-16 for Remote Sensing Image Classification

EOM-NPOSESs: Emergency Ontology Model Based on Network Public Opinion Spread Elements

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