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Optimal Slot Assignment for Binary Tracking Tree Protocol in RFID Tag Identification

机译:RFID标签识别中的二进制跟踪树协议的最佳插槽分配

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Tag anti-collision has long been an important issue in RFID systems. To accelerate tag identification, some researchers have recently adopted bit tracking technology that allows the reader to detect the locations of collided bits in a collision slot. However, these methods still encounter the problem of too many collisions occurring at the beginning of identification. This paper proposes an optimal binary tracking tree protocol (OBTT) that tries to separate all of the tags into smaller sets to reduce collisions at the beginning of identification. Using bit tracking technology, OBTT mainly adopts three proposed approaches, bit estimation, optimal partition, and binary tracking tree. Bit estimation first estimates the number of tags based on the locations of collided bits. Optimal partition then determines the optimal number of the initial sets based on this estimation. Binary tracking tree lets the tag utilize one counter to achieve the split during the identification process. This paper formally analyzes the slot efficiency of OBTT, which represents how many tags can be identified in a slot. Results show that the slot efficiency is close to 0.614, the highest value published to date. Considering slot lengths, OBTT further determines the optimal number of the initial sets to minimize the identification delay. The analytical results show that the delay efficiency of OBTT achieves 0.750, where delay efficiency represents the number of tags that can be identified in a baseline slot, the length of which is the complete ID sent by the tag. The simulation results show that OBTT outperforms other existing algorithms.
机译:长期以来,标签防撞一直是RFID系统中的重要问题。为了加速标签识别,一些研究人员最近采用了位跟踪技术,该技术使阅读器可以检测碰撞槽中碰撞位的位置。但是,这些方法仍然遇到在识别开始时发生太多碰撞的问题。本文提出了一种最佳的二进制跟踪树协议(OBTT),该协议试图将所有标签分成较小的集合,以减少识别开始时的冲突。 OBTT使用位跟踪技术,主要采用三种建议的方法,即位估计,最佳划分和二进制跟踪树。位估计首先根据冲突位的位置估计标签的数量。最佳划分然后基于该估计确定初始集合的最佳数目。二进制跟踪树使标签可以在识别过程中利用一个计数器来实现拆分。本文正式分析了OBTT的插槽效率,它代表了一个插槽中可以识别多少个标签。结果表明,插槽效率接近0.614,是迄今为止发布的最高值。考虑时隙长度,OBTT进一步确定初始集合的最佳数量,以最大程度地减少识别延迟。分析结果表明,OBTT的延迟效率达到0.750,其中延迟效率表示可以在基线槽中识别的标签数量,其长度是标签发送的完整ID。仿真结果表明,OBTT优于其他现有算法。

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