Cardinality Estimation for Largescale RFID Systems

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Cardinality Estimation for Large-scale RFID
Systems

• Chen Qian, Hoilun Ngan, and Yunhao Liu
• Hong Kong University of Science and Technology

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RFID Hot Topic

• Both in industry and academic society
• RFID independent sessions (three or more papers)
  in PerCom 2007, 2008
• 2009?

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Research Issues(take our group as an example)

• Localization

• Object Tracking

• Security Privacy

• Tag Counting Estimation

To be expanded
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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
Z. Zhou, et. al., "Slotted Scheduled Tag Access
in Multi-Reader RFID Systems", ICNP 2007
Qunfeng Dong, et. al., Load Balancing in
Large-Scale RFID Systems, Infocom 2007
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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.
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Real Problems

• RFID tags are used to label large-volume items.
• Hence, collecting the information of these items
  is the main goal of the RFID system.
• Two main kinds of information
• Identities Cardinality

Identification
Counting
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IdentificationALOHA
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IdentificationTree
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Tag countingSome applications

• Hong Kong International Airport

Cargo transportations
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Tag countingSome applications

• Stadium RFID System

Security and traffic control
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IdentificationLimitation

• We can obtain the tag cardinality via
  identification.
• But.
• Extremely long latency
• 1000 sec for 3000 tags
• Not applicable for mobile objects

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Estimation(Mobicom 06)
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EstimationLimitation

• Multiple-reading problem

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Our Goal

• Design an estimation scheme that can
• Eliminate replications from the sum of reader
  results.
• Achieve a short processing time,
• And high accuracy.

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LPE

• Linear Probabilistic Estimation (LPE)

Replication-insensitive
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LPELimitation

• Processing time is still too long to be ideal
• One can never know in advance that how long the
  ALOHA frame should be set.

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• Can we design an estimation scheme
• that works well without pre-knowledge?

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Galton Board
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GD Galton Board

• Geometric Distribution

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LoF

• Lottery Frame

Approximately 1/2(t1) of the tag responses are
in time slot t.
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LoF
The kth bit in bitmap BMk will be zero if
kgtgtlog2n, or be one if kltltlog2n. The fringe
consists zeros and ones for the k whose value is
near log2n.
R is the position of the right most zero
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LoF
P. Flajolet and G. N. Martin, "Probabilistic
Counting Algorithms for Data Base Applications,"
Journal of Computer and System Science, vol. 31,
1985.
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LoFaccuracy

• LoF estimation may not be accurate enough for
  some applications.
• Luckily the right most zero R is an unbiased
  estimator of log2n, which means

If we make several independent estimations and
compute the average result, the standard error
will be reduced.
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LoFmultiple hashes
Consider the average value
The variable has the expectation and
standard deviation that satisfy

Therefore, the improved estimator is
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LoFaccuracy
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LoFprocessing 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
216 65536 tags.
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Simulationsetup
Fixed 32-slot length for LoF estimation.
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SimulationSingle reader
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SimulationSingle reader
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SimulationMultiple reader
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SimulationProcessing time
Just the last time!
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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.

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• Questions?
• Thank you !