静态朴素MAC支持下RFID网络的认知完成时间分析

Heewon Seo, Jun Ha, J. Park, C. Choi
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引用次数: 0

摘要

我们考虑一个RFID网络,该网络由单个读取器和驻留在读取器附近的多个标签组成。在这种RFID网络中,两个或多个标签同时发送的数据包之间会发生冲突。为了在支持阅读器识别标签的同时解决冲突问题,我们考虑了一种静态朴素MAC方案,该方案采用基于帧槽ALOHA的冲突仲裁方法。在RFID网络中,阅读器的基本任务是在规定的时间内识别每个相邻的标签。因此,有必要对认知完成时间进行分析,例如,读者认识所有标签所经过的时间。本文首先用数学方法将认知完成时间描述为有限状态空间上齐次非递减马尔可夫链的撞击时间。其次,我们利用具有随机优势的随机变量序列在同一状态空间上构造了一个快速收敛的马尔可夫链。最后,我们提出了一种数值上有效的方法,该方法将认知完成时间近似为快速收敛马尔可夫链的撞击时间。数值算例证实,认知完成时间的近似期望值为精确期望值提供了一个下界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Cognition Completion Time in RFID Networks Supported by Static Naive MAC Scheme
We consider an RFID network that consists of a single reader and multiple tags sojourning in the vicinity of the reader. In such an RFID network, collisions occur among the packets which are simultaneously sent by two or more tags. In order to support the reader to cognize tags while resolving the collision problem, we consider a static naive MAC scheme that employs a collision arbitration method rooted in framed slotted ALOHA. A basic mission of a reader in an RFID network is to cognize every neighboring tag in a prescribed time. Thus, it is of necessity to analyze the cognition completion time, e.g., the time elapsed until the reader cognized all the tags. In this paper, we first mathematically describe the cognition completion time as a hitting time of a homogeneous non-decreasing Markov chain on a finite state space. Next, we construct a fast converging Markov chain on the same state space by using a sequence of random variables that have stochastic dominance. Finally, we propose a numerically efficient method in which the cognition completion time is approximated by a hitting time of the fast converging Markov chain. Numerical examples confirm that the approximate expected value of the cognition completion time provides a lower bound on the exact expected value.
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