RFID: Hot Topic - Department of Computer Sciences

cribabsurdElectronics - Devices

Nov 27, 2013 (3 years and 10 months ago)

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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.

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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

   



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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

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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 !