@article{MADEEDA6B,
title = "Bounds on Expected Cognition Completion Time in RFID Networks Employing Static Naive MAC Scheme",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.1.31",
author = "CheonWon Choi",
keywords = "RFID network, Static naive MAC, Framed slotted ALOHA, Cognition completion time, First-order stochastic dominance, Fast-converging Markov chain",
abstract = "Consider an RFID network that consists of single reader and multiple tags residing in the vicinity of the reader. In the RFID network, a collision can occur among the packets which are almost simultaneously sent by two or more tags. For numerous tags to be able to successfully deliver their packets to the reader while arbitrating the collision among some packets, suppose that the RFID network employs a static naive MAC scheme rooted in framed slotted ALOHA. Without prior information about nearby tags, a single-reader multiple-tag RFID is often deployed to cognize neighboring tags, i.e., to gather the identification numbers of the tags attached on various items. Definitely, it is of necessity to cognize all the tags in a limited time.
Thus, it is of utmost importance to investigate the cognition completion time, i.e., the time elapsed until the reader cognizes all the tags. In this paper, we first construct a Markov chain to represent the cognition completion time as a hitting time of the Markov chain. As an alternative to the exact value of the expected cognition completion time, which is hardly obtainable in a tractable form, we then develop a lower bound by building a fast-converging Markov chain with first-order stochastically dominating random variables and attain an upper bound by constructing slow-converging Markov chains with first-order stochastically dominated random variables. Numerical examples reveal that the exact value of the expected cognition completion time is tightly bounded above by the upper bound in case a relatively small number of slots comprise each response interval. Further, the examples corroborate that the lower and upper bounds exhibit a parametric characteristic of convexity as similarly as the exact value of the expected cognition completion time does."
}