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Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification.

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Title: Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification.
Authors: Zhao, Min1 (AUTHOR) 19935104665@163.com
Source: International Journal of RF Technologies: Research & Applications. Aug2025, Vol. 15 Issue 2, p86-106. 21p.
Subjects: Wireless sensor networks, Smart structures, Position sensors, Sensor placement, Data mining, Radio frequency identification systems
Abstract: Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of RF Technologies: Research & Applications is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification.
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Min%22">Zhao, Min</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 19935104665@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+RF+Technologies%3A+Research+%26+Applications%22">International Journal of RF Technologies: Research & Applications</searchLink>. Aug2025, Vol. 15 Issue 2, p86-106. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+structures%22">Smart structures</searchLink><br /><searchLink fieldCode="DE" term="%22Position+sensors%22">Position sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+placement%22">Sensor placement</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Radio+frequency+identification+systems%22">Radio frequency identification systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of International Journal of RF Technologies: Research & Applications is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/17545730241297787
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        Text: English
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        PageCount: 21
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      – SubjectFull: Wireless sensor networks
        Type: general
      – SubjectFull: Smart structures
        Type: general
      – SubjectFull: Position sensors
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      – SubjectFull: Sensor placement
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      – SubjectFull: Data mining
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      – SubjectFull: Radio frequency identification systems
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              Text: Aug2025
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