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LocP: An efficient Localized Polling Protocol for large-scale RFID systems

机译:LocP:适用于大型RFID系统的高效本地轮询协议

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RFID systems nowadays are operated at large-scale in terms of both occupied space and tag quantity. One may have prior knowledge of the complete set of tags (denoted by N) and any set of wanted tags (denoted by M) within the complete set, i.e., M ⊆ N. Then here comes an open problem: when one is particularly interested in a subarea of the system, how to collect information (not simply tagIDs) from a wanted subset (denoted by dM) of the interrogated tags (denoted by dN) in that subarea? This issue has great significance in many practical applications but appears to be challenging when there is a stringent time constraint. In this work, we first establish the lower-bound of this problem, and show a straightforward polling solution. Then, we propose a novel polling protocol called LocP, which consists of two phases: the Tags-Filtering phase and the Ordering-and-Reporting phase. LocP employs Bloom Filter twice to significantly reduce the scale of candidate tags in the Tags-Filtering phase. In the Ordering-and-Reporting phase, tags determine their own transmission time-slots according to the allocation vectors iteratively broadcasted by the reader. LocP thus achieves a delicate tradeoff between time and polling accuracy. We conduct extensive simulations to evaluate the performance of LocP. The results demonstrate that LocP is highly efficient in terms of information collection time, leading to convincing applicability and scalability of large-scale RFID systems.
机译:如今,RFID系统无论是在占用空间还是在标签数量上都可以大规模使用。一个人可能已经对标签的完整集合(用N表示)和该完整集合中的任何有用标签的集合(用M表示)都具有先验知识,即M⊆N。这就是一个开放的问题:当一个人特别感兴趣在系统的子区域中,如何从该子区域中被询问标签(由dN表示)的通缉子集(由dM表示)中收集信息(而不仅仅是tagID)?这个问题在许多实际应用中具有重大意义,但是在严格的时间限制下似乎具有挑战性。在这项工作中,我们首先确定该问题的下限,并显示一个简单的轮询解决方案。然后,我们提出了一种新颖的轮询协议,称为LocP,它由两个阶段组成:标记过滤阶段和排序与报告阶段。 LocP两次使用布隆过滤器,以在标签过滤阶段显着减小候选标签的规模。在订购和报告阶段,标签根据阅读器反复广播的分配矢量确定其自己的传输时隙。因此,LocP在时间和轮询精度之间实现了微妙的权衡。我们进行了广泛的仿真,以评估LocP的性能。结果表明,LocP在信息收集时间方面非常高效,从而使大型RFID系统的适用性和可伸缩性令人信服。

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