1
Cardinality
Estimation for
Large

scale
RFID
Systems
Chen Qian
, Hoilun Ngan, and Yunhao Liu
Hong Kong University of Science and Technology
2
RFID: Hot Topic
•
Both in industry and academic society
•
RFID: independent sessions (three or
more papers) in PerCom 2007, 2008
•
2009?
3
Research Issues
(take our group as an example)
•
Localization
•
Object Tracking
•
Security & Privacy
•
Tag Counting & Estimation
To be expanded…
4
RFID: Hot Topic
•
Some RFID papers in other top confs.
M. S. Kodialam, T. Nandagopal, “Fast and reliable
estimation schemes in RFID systems”,
MobiCom
2006
J. Myung, W. Lee, “Adaptive splitting protocols for
RFID tag collision arbitration”,
MobiHoc
2006
Qunfeng Dong, et. al., “Load Balancing in Large

Scale
RFID Systems”,
Infocom
2007
Z. Zhou, et. al., "Slotted Scheduled Tag Access in
Multi

Reader RFID Systems",
ICNP
2007
5
RFID Sys. Model
RFID Readers
–
Carrying antennas,
collect info from
nearby tags.
–
Connected with
servers
RFID Tags
–
Labeled with
unique serial #s
–
Simple structure
–
Large

deployed,
but can not
communicate
with each other
If multiple tags transmit to reader
simultaneously, a collision happens,
and reader cannot recognize these tags.
6
Real Problems
•
RFID tags are used to label
large

volume
items
.
•
H
ence,
collecting
th
e
information
of
these items
is the main goal of
the
RFID system.
•
Two main kinds of information:
–
Identities Cardinality
Identification
Counting
9
Tag counting:
Some applications
•
Hong Kong International Airport
Cargo
transportations
10
Tag counting:
Some applications
•
Stadium RFID System
Security and
traffic control
11
Identification:
Limitation
•
We can obtain the tag cardinality via
identification.
But….
Extremely long latency
–
1000 sec for 3000 tags
Not applicable for mobile objects
12
Estimation
(Mobicom 06)
13
Estimation:
Limitation
•
Multiple

reading problem
14
Our Goal
•
Design an estimation scheme that can
–
Eliminate replications from the sum of
reader results.
–
Achieve a short processing time,
–
And high accuracy.
15
LPE
•
Linear Probabilistic Estimation (LPE)
Replication

insensitive
16
LPE:
Limitation
•
Processing time is still too long to be
ideal
•
One can never know in advance that
how long the ALOHA frame should be
set.
17
Can we design an estimation scheme
that works well without pre

knowledge?
18
Galton Board
19
GD Galton Board
Geometric Distribution
20
LoF
•
Lottery Frame
Approximately
1/2
(
t
+1)
of the tag
responses are in
time slot
t.
21
LoF
The
k
th bit in bitmap
BM
[
k
] will be zero if
k
>>log2
n
, or be one if
k
<<log2
n
.
The fringe consists zeros
and ones for the
k
whose
value is near log
2
n
.
2
R
n
R
is the position of the right most zero
22
LoF
P. Flajolet and G. N. Martin,
"Probabilistic Counting
Algorithms for Data Base
Applications,"
Journal of
Computer and System
Science
, vol. 31, 1985.
1.2897
1.2897 2
R
n
23
LoF:
accuracy
•
LoF estimation may not be accurate
enough for some applications.
•
Luckily the right most zero
R
is an
unbiased estimator
of log
2
n
, which
means
If we make several independent estimations
and compute the average result, the standard
error will be reduced.
24
LoF:
multiple hashes
1 2
...
m
R R R
R
m
R
Consider the average value
The variable
has the expectation and standard deviation
that satisfy
( )/
R m
Therefore, the improved estimator is
/
1.2897 2 1.2897 2
i
i
R m
R
n
25
LoF:
accuracy
26
LoF:
processing time
The number of time slots required
for a frame is independent from the
size of tag set.
A frame with 16 slots is enough to
estimate up to 2
16
= 65536 tags.
27
Simulation:
setup
Fixed 32

slot length for LoF estimation.
28
Simulation:
Single reader
29
Simulation:
Single reader
0
10000
20000
30000
40000
50000
0.1
1
10
Number of Tags
Normalized Standard Deviation
LoF (1 hash)
LoF (2 hashes)
LoF (4 hashes)
LoF (8 hashes)
LoF (16 hashes)
LoF (32 hashes)
30
Simulation:
Multiple reader
50
55
60
65
70
0
0.25
0.5
0.75
1
Reading Range (units)
Error
UPE (Max)
UPE (Sum)
LoF (16 hashes)
LoF (64 hashes)
31
Simulation:
Processing time
Just the
last time!
32
Summary
•
LoF is a replication

insensitive
estimation, working well in multi

reader environments.
•
LoF can obtain higher accuracy and
lower latency, comparing with
previous schemes.
•
Trade

off in LoF: the storage for
hash functions.
33
•
Questions?
Thank you !
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