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Cardinality Estimation for Large-scale RFID Systems

Cardinality Estimation for Large-scale RFID Systems. Chen Qian , Hoilun Ngan, and Yunhao Liu Hong Kong University of Science and Technology. 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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Cardinality Estimation for Large-scale RFID Systems

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  1. Cardinality Estimation for Large-scale RFIDSystems 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 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

  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. • Hence, collecting the information of these items is the main goal of the RFID system. • Two main kinds of information: • Identities Cardinality Identification Counting

  7. Tag counting:Some applications • Hong Kong International Airport Cargo transportations

  8. Tag counting:Some applications • Stadium RFID System Security and traffic control

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

  10. Estimation(Mobicom 06)

  11. Estimation:Limitation • Multiple-reading problem

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

  13. LPE • Linear Probabilistic Estimation (LPE) Replication-insensitive

  14. 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.

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

  16. Galton Board

  17. GD Galton Board Geometric Distribution

  18. LoF • Lottery Frame Approximately 1/2(t+1)of the tag responses are in time slot t.

  19. LoF The kth bit in bitmap BM[k] will be zero if k>>log2n, or be one if k<<log2n. The fringe consists zeros and ones for the k whose value is near log2n. R is the position of the right most zero

  20. LoF P. Flajolet and G. N. Martin, "Probabilistic Counting Algorithms for Data Base Applications," Journal of Computer and System Science, vol. 31, 1985.

  21. LoF:accuracy • 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.

  22. LoF:multiple hashes Consider the average value The variable has the expectation and standard deviation that satisfy Therefore, the improved estimator is

  23. LoF:accuracy

  24. 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 216 = 65536 tags.

  25. Simulation:setup Fixed 32-slot length for LoF estimation.

  26. Simulation:Single reader

  27. Simulation:Single reader

  28. Simulation:Multiple reader

  29. Simulation:Processing time Just the last time!

  30. 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.

  31. Questions? Thank you !

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