Research on the RFID Anticollision Strategy Based on Decision Tree

Zhang, Hui; Gao, Ling; Luo, Hanguang; Zhai, Ya

doi:10.1155/2022/2913157

Cited by 4 publications

(3 citation statements)

References 13 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e text classification method in this study adopts a naive Bayesian classification model and decision tree classification model [28][29][30][31][32][33][34][35][36][37].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Public Big Data Management under Text Analysis

Zhu

Kan

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Based on text analysis, public big data management is studied. The public data management of Mount Wutai tourism network travel notes is discussed. The positive, neutral, and negative effects of the naive Bayesian classification model and decision tree classification model on the tourism sentiment attitude of Mount Wutai are compared. The relationship between tourism resources, tourism facilities, tourism services, tourism environment, and tourism sentiment and attitude of Wutai Mountain is analyzed. The results show that the true positive rate, true negative rate, and F-measure of the Bayesian decision tree classifier to classify positive text are 86.64%, 81.27%, and 84.62%, respectively. The true positive rate for neutral text is 82.05%, the true negative rate is 78.89%, and the F-measure is 77.11%. The true positive rate for negative text is 83.67%, the true negative rate is 98.29%, and the F-measure is 82.83%. The Bayesian decision tree classifier can evaluate positive and negative texts better than neutral texts. The true positive rate of the C4.5 decision tree classifier for positive text is 91.44%, the true negative rate is 86.57%, and the F-measure is 89.45%. The true positive rate for neutral text is 90.17%, the true negative rate is 83.28%, and the F-measure is 84.06%. The true positive rate for negative text is 91.84%, the true negative rate is 99.05%, and the F-measure is 90.91%. The decision tree classifier has a better evaluation effect on positive and negative texts than on neutral texts. The ROC curve of the evaluation effect of the two classifiers shows that the evaluation effect of the two classifiers has a better evaluation effect on positive text than that of the neutral and negative texts, and the evaluation effect of the C4.5 decision tree classifier is better than that of the Bayesian classifier. The promotion degree of tourism resources and facilities in forwarding online travel notes is obviously higher, and there is a high correlation between tourism resources and facilities and forward online travel notes. In negative online travel notes, the promotion degree of tourism service and tourism environment is high, and the correlation between tourism service and tourism environment and negative online travel notes is high. In summary, improving the quality of tourism services and the tourism environment of Mount Wutai scenic spots can better enhance the recognition and satisfaction of tourists with Mount Wutai tourism.

show abstract

“…e text classification method in this study adopts a naive Bayesian classification model and decision tree classification model [28][29][30][31][32][33][34][35][36][37].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Public Big Data Management under Text Analysis

Zhu

Kan

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…When multiple tags have a long common prefix, the reader needs to receive multiple conflicting signals in order to break up the relevant tags into smaller groups. To this end, researchers have successively proposed a number of improvements [27][28][29][30][31][32][33]. Among them, the literature [27] proposes a dynamic multi-ary query tree collision protocol for RFID systems which can completely eliminate empty slots and greatly reduce collision slots.…”

Section: Research Status At Home and Abroadmentioning

confidence: 99%

A Review of Tags Anti-Collision Identification Methods Used in RFID Technology

Wang,

Luo,

Guo

et al. 2023

Electronics

View full text Add to dashboard Cite

With radio frequency identification (RFID) becoming a popular wireless technology, more and more relevant applications are emerging. Therefore, anti-collision algorithms, which determine the time to tag identification and the accuracy of identification, have become very important in RFID systems. This paper presents the algorithms of ALOHA for randomness, the binary tree algorithm for determinism, and a hybrid anti-collision algorithm that combines these two algorithms. To compensate for the low throughput of traditional algorithms, RFID anti-collision algorithms based on blind source separation (BSS) are described, as the tag signals of RFID systems conform to the basic assumptions of the independent component analysis (ICA) algorithm. In the determined case, the ICA algorithm-based RFID anti-collision method is described. In the under-determined case, a combination of tag grouping with a blind separation algorithm and constrained non-negative matrix factorization (NMF) is used to separate the multi-tag mixing problem. Since the estimation of tag or frame length is the main step to solve the RFID anti-collision problem, this paper introduces an anti-collision algorithm based on machine learning to estimate the number of tags.

show abstract

“…Anti-collision algorithms are divided into two categories: the tree-based algorithm [ 17 , 18 , 19 ] (including query tree and tree splitting) and the ALOHA-based algorithm [ 20 , 21 , 22 , 23 ]. The former requires high computing costs when solving the problem of tag starvation.…”

Section: Introductionmentioning

confidence: 99%

Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

Xu,

Song,

Sun

et al. 2023

Sensors

View full text Add to dashboard Cite

Passive radio-frequency identification (RFID) systems have been widely applied in different fields, including vehicle access control, industrial production, and logistics tracking, due to their ability to improve work quality and management efficiency at a low cost. However, in an intersection situation where tags are densely distributed with vehicle gathering, the wireless channel becomes extremely complex, and the readers on the roadside may only decode the information from the strongest tag due to the capture effect, resulting in tag misses and considerably reducing the performance of tag identification. Therefore, it is crucial to design an efficient and reliable tag-identification algorithm in order to obtain information from vehicle and cargo tags under adverse traffic conditions, ensuring the successful application of RFID technology. In this paper, we first establish a Nakagami-m distributed channel capture model for RFID systems and provide an expression for the capture probability, where each channel is modeled as any relevant Nakagami-m distribution. Secondly, an advanced capture-aware tag-estimation scheme is proposed. Finally, extensive Monte Carlo simulations show that the proposed algorithm has strong adaptability to circumstances for capturing under-fading channels and outperforms the existing algorithms in terms of complexity and reliability of tag identification.

show abstract

Research on the RFID Anticollision Strategy Based on Decision Tree

Cited by 4 publications

References 13 publications

Analysis of Public Big Data Management under Text Analysis

Analysis of Public Big Data Management under Text Analysis

A Review of Tags Anti-Collision Identification Methods Used in RFID Technology

Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

Contact Info

Product

Resources

About