NDLI: Joint property estimation for multiple RFID tag sets using snapshots of variable lengths

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Joint property estimation for multiple RFID tag sets using snapshots of variable lengths

Content Provider	ACM Digital Library
Author	Xiao, Qingjun Chen, Min Chen, Shigang
Abstract	Radio-frequency identification (RFID) technology has been widely adopted by real-world industries. This paper presents a new application for distributively deployed RFID systems, wherein a user chooses multiple tag sets at will from different spatial or temporal domains, and then connects them by set operators (union, intersection and relative complement) to form a set expression. The user is allowed to query for the cardinality of an arbitrary set expression, which is called the joint property of multiple sets. We focus on the problem of estimating the joint property with bounded error, which has many potential applications. One of them is to allow users to check the number of tags in an arbitrary tag flow passing through a distributed RFID system. For this problem, we propose a solution with a novel design that supports versatile snapshot construction: Given the snapshots of multiple tag sets, although their lengths may be very different, our formulas can estimate their joint properties, with an accuracy that can be arbitrarily set. For the proposed estimator, we formally analyze its bias and variance, and also the optimal settings of protocol parameters to minimize the time cost of taking a snapshot of a tag set. The simulation results show that, under predefined accuracy requirement, our solution can reduce time cost by multiple folds as compared with existing works named DiffEstm and CCF, which require all tag sets must be encoded into snapshots with an equal length.
Starting Page	151
Ending Page	160
Page Count	10
File Format	PDF
ISBN	9781450341844
DOI	10.1145/2942358.2942377
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cardinality estimation Random hashing Rfid
Content Type	Text
Resource Type	Article

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