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A tags’ arrival rate estimation method using weighted grey model(1,1) and sliding window in mobile radio frequency identification systems

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  • Liqian Zhang
  • Xueliang Fu
  • Honghui Li

Abstract

In order to guarantee the tag identification accuracy and efficiency in mobile radio frequency identification system, it is necessary to estimate the tags’ arrival rate before performing identification. This research aims to develop a novel estimation method based on improved grey model(1,1) and sliding window mechanism. By establishing tags’ dynamic arrival model, this article emphasizes the importance of tags’ arrival rate estimation in mobile radio frequency identification system. Using sliding window mechanism and weighted coefficients method, weighted grey model(1,1) with sliding window (WGMSW(1,1)) is proposed based on traditional grey model(1,1). For experimental verification, three kinds of data are used as original data in WGMSW(1,1). The experimental results show that the proposed method has lower estimation error rate, lower computation complexity, and high system stability.

Suggested Citation

  • Liqian Zhang & Xueliang Fu & Honghui Li, 2020. "A tags’ arrival rate estimation method using weighted grey model(1,1) and sliding window in mobile radio frequency identification systems," International Journal of Distributed Sensor Networks, , vol. 16(10), pages 15501477209, October.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:10:p:1550147720967894
    DOI: 10.1177/1550147720967894
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    References listed on IDEAS

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    1. Yi-Chung Hu, 2017. "Electricity consumption prediction using a neural-network-based grey forecasting approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1259-1264, October.
    2. Liqian Zhang & Xueliang Fu & Honghui Li, 2019. "Round-priority-based anti-collision tag identification method in a mobile radio-frequency identification system," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.
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