• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.9
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• pp.29-36
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• 2010

Consider an RFID network configured as a star such that a single reader is surrounded by a crowd of tags. In the RFID network, prior to attaining the information stored at a tag, the reader must cognize the tags while arbitrating a collision among tags' responses. For this purpose, we present a tag cognizance scheme based on framed and slotted ALOHA, which statically provides a number of slots in each frame for the tags to respond. For the evaluation of the cognizance performance, we choose the cognizance completion probability and the expected cognizance completion time as key performance measures. Then, we present a method to numerically calculate the performance measures. Especially, for small numbers of tags, we derive them in a closed form. Next, we formulate a problem to find an optimal time structure which either maximizes the cognizance completion probability under a constraint on the cognizance time or minimizes the expected cognizance completion time. By solving the problem, we finally obtain an optimal number of slots per frame for the tags to respond. From numerical results, we confirm that there exist a finite optimal number of slots for the tags to respond. Also, we observe that the optimal number of slots maximizing the cognizance completion probability tends to approach to the optimal number of slots minimizing the expected cognizance completion time as the constraint on the cognizance time becomes loose.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.2
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• pp.21-28
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• 2011

We consider an RFID network configured as a star in which tags stationarily move into and out of the vicinity of the reader. To cognize the neighboring tags in the RFID network, we propose a scheme based on dynamic framed and slotted ALOHA which determines the number of slots belonging to a frame in a dynamic fashion. The tag cognizance scheme distinctively employs a rule for estimating the expected number of neighboring tags, identified as R-retrospective maximum likelihood rule, where the observations attained in the R previous frames are used in maximizing the likelihood of expected number of tags. Simulation result shows that a slight increase in depth of retrospect is able to significantly improve the cognizance performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1674-1692
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• 2018

An RFID network is a network in which a reader inquire about the identities of tags and tags respond with their identities to a reader. The diversity of RFID networks has brought about many applications including an inexpensive system where a single reader supports a small number of tags. Such a system needs a tag cognizance scheme that is able to arbitrate among contending tags as well as is simple enough. In this paper, confining our attention to a clan of simple schemes, we propose a randomized scheme with aiming at enhancing the tag cognizance rate than a conventional scheme. Then, we derive an exact expression for the cognizance rate attained by the randomized scheme. Unfortunately, the exact expression is not so tractable as to optimize the randomized scheme. As an alternative way, we develop an upper bound on the tag cognizance rate. In a closed form, we then obtain a nearly optimal value for a key design parameter, which maximizes the upper bound. Numerical examples confirm that the randomized scheme is able to dominate the conventional scheme in cognizance rate by employing a nearly optimal value. Furthermore, they reveal that the randomized scheme is robust to the fallacy that the reader believes or guesses a wrong number of neighboring tags.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.70-77
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• 2009

In an RFID network consisting of a single reader and many tags, a framed and slotted ALOHA, which provides a number of slots for the tags to respond, was introduced for arbitrating a collision among tags' responses. In a framed and slotted ALOHA, the number of slots in each frame should be optimized to attain the maximal efficiency in tag cognizance. While such an optimization necessitates the knowledge about the number of tags, the reader hardly knows it. In this paper, we propose a tag cognizance scheme based on framed and slotted ALOHA, which is characterized by directly taking a Bayes action on the number of slots without estimating the number of tags separately. Specifically, a Bayes action is yielded by solving a decision problem which incorporates the prior distribution the number of tags, the observation on the number of slots in which no tag responds and the loss function reflecting the cognizance rate. Also, a Bayes action in each frame is supported by an evolution of prior distribution for the number of tags. From the simulation results, we observe that the pair of evolving prior distribution and Bayes action forms a robust scheme which attains a certain level of cognizance rate in spite of a high discrepancy between the Due and initially believed numbers of tags. Also, the proposed scheme is confirmed to be able to achieve higher cognizance completion probability than a scheme using classical estimate of the number of tags separately.