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Model of dynamic clustering-based energy-efficient data filtering for mobile RFID networks
ETRI Journal ( IF 1.4 ) Pub Date : 2021-04-01 , DOI: 10.4218/etrij.2020-0009
Viet Minh Nhat Vo 1 , Van Hoa Le 1
Affiliation  

Data filtering is an essential task for improving the energy efficiency of radio-frequency identification (RFID) networks. Among various energy-efficient approaches, clustering-based data filtering is considered to be the most effective solution because data from cluster members can be filtered at cluster heads before being sent to base stations. However, this approach quickly depletes the energy of cluster heads. Furthermore, most previous studies have assumed that readers are fixed and interrogate mobile tags in a workspace. However, there are several applications in which readers are mobile and interrogate fixed tags in a specific area. This article proposes a model for dynamic clustering-based data filtering (DCDF) in mobile RFID networks, where mobile readers are re-clustered periodically and the cluster head role is rotated among the members of each cluster. Simulation results show that DCDF is effective in terms of balancing energy consumption among readers and prolonging the lifetime of the mobile RFID networks.

中文翻译:

基于动态聚类的移动RFID网络节能数据过滤模型

数据过滤是提高射频识别 (RFID) 网络能效的一项基本任务。在各种节能方法中,基于簇的数据过滤被认为是最有效的解决方案,因为来自簇成员的数据可以在发送到基站之前在簇头进行过滤。然而,这种方法很快耗尽了簇头的能量。此外,大多数先前的研究都假设阅读器是固定的并在工作空间中询问移动标签。然而,在一些应用中,阅读器是移动的,可以查询特定区域内的固定标签。本文提出了一种移动RFID网络中基于动态聚类的数据过滤(DCDF)模型,其中移动阅读器定期重新聚类,簇头角色在每个簇的成员之间轮换。仿真结果表明,DCDF 在平衡阅读器之间的能耗和延长移动 RFID 网络的寿命方面是有效的。
更新日期:2021-04-01
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