THESIS
2015
xii, 86 pages : illustrations ; 30 cm
Abstract
Radio Frequency Identification (RFID) attracts increasing attention in the recent years
due to its good application prospect. It is widely used in a variety of applications such
as warehouse management, inventory control, object tracking and localization, etc. RFID
devices, especially tags, have small size and ultra-low power consumption. With such advantages,
they are well-suited to automatic inventory management in a large-scale. In practice,
large-scale management in RFID systems is primarily comprised of two mainstreams, namely
tag identification and estimation. Identification is a basic operation of collecting tag IDs to
identify corresponding objects. Estimation aims to count the number of tags quickly and accurately.
In this dissertation, we explore how to design effectiv...[
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Radio Frequency Identification (RFID) attracts increasing attention in the recent years
due to its good application prospect. It is widely used in a variety of applications such
as warehouse management, inventory control, object tracking and localization, etc. RFID
devices, especially tags, have small size and ultra-low power consumption. With such advantages,
they are well-suited to automatic inventory management in a large-scale. In practice,
large-scale management in RFID systems is primarily comprised of two mainstreams, namely
tag identification and estimation. Identification is a basic operation of collecting tag IDs to
identify corresponding objects. Estimation aims to count the number of tags quickly and accurately.
In this dissertation, we explore how to design effective protocols to build large-scale
RFID management systems. The protocols, as introduced above, address two fundamental
problems in RFID systems, namely identification and estimation. Both type of protocols
should scale well to massive tags.
In our first work, we investigate how to efficiently identify a large amount of tags with
one mobile reader that continuous changes its position to expand the coverage, denoted as
the continuous scanning problem. We observe that the performance of continuous scanning
protocol depends on the spatial distribution of tags in two adjacent scans. An adaptive
continuous scanning protocol is proposed that selects the best scanning strategy according
to the current spatial distribution of tags.
In our second work, we study the conventional RFID estimation problem. We notice that
existing estimation approaches merely provide asymptotic results of estimation time, but
fail to give tight bounds for the convergence rate of corresponding algorithms. We propose
an estimation scheme that achieves Arbitrarily Accurate Approximation (A
3) for the tag population size. More importantly, we give an rigorous bound O((loglog n + ∈
-2)logδ
-1) in its communication time, for a given (∈,δ) accuracy requirement.
In our last work, we explore a generalized RFID estimation problem named Generic
Composite Counting. The conventional RFID estimation problem focuses on counting the
number of tags in a single tag set, or at most the union of multiple tag sets. This simple
scenario is far from enough to meet various application demands. To address this problem,
we introduce a more complex counting model, which aims to estimate the cardinality of
a composite set expression such as (S
1∪S
2) - S
3, where S
i (1 ≤ i ≤ 3) denotes a tag
set. A Composite Counting Framework (CCF) is designed to provide estimates for any set
expression with desired (∈,δ) accuracy.
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