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Probabilistic Optimal Tree Hopping for RFID Identification

Probabilistic Optimal Tree Hopping for RFID Identification. Muhammad Shahzad Alex X. Liu Dept. of Computer Science and Engineering Michigan State University East Lansing, Michigan, 48824, USA. RFID is everywhere. Radio Frequency Identification. 0000. 0110. 0101.

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Probabilistic Optimal Tree Hopping for RFID Identification

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  1. Probabilistic Optimal Tree Hoppingfor RFID Identification Muhammad Shahzad Alex X. Liu Dept. of Computer Science and Engineering Michigan State University East Lansing, Michigan, 48824, USA

  2. RFID is everywhere

  3. Radio Frequency Identification 0000 0110 0101 1000 0011 1001 1011 1010 1101

  4. Tree Walking (EPCGlobal Standard) 0 1 1 8 00 10 01 11 5 9 16 2 011 000 010 100 101 001 4 6 7 3 10 13 1000 1001 1010 1011 11 12 14 15 Number of queries: 16

  5. Optimizing Tree Walking Total queries = successful + collisions + empty Minimize total queries

  6. Limitations of Prior Art All prior work proposes heuristics to reduce identification time • MobiHoc’06, PerCom’07, INFOCOM’09, ICDCS’10 No formal model of the Tree Walking process • No optimality results

  7. Our Modeling of Tree Walking Position p n=16 Level l m=4 • equals the probability that parent of node is a collision (Hypergeometric distribution)

  8. Proposed Approach Estimate unidentified tag population size Find optimal level and the first unvisited node Perform Tree Walking. Go to step 1

  9. Population Size Estimation First time estimation: rough, but fast • We adapt a fast scheme proposed by Flajolet and Martin in the database community in 1985. • Did not use accurate RFID estimation schemes Subsequent estimation = estimated tags - identified tags

  10. Calculating Optimal Level • Calculate if we start from level between and • Minimize to obtain optimal

  11. Effect of obtaining optimal

  12. Tree Hopping vs. Tree Walking

  13. Tree Hopping Example 11 11 000 001 010 011 100 101 2 4 1 3 5 8 1000 1001 1010 1011 6 7 9 10 Number of queries: 11 (compared to 16 of TW)

  14. Experimental Evaluation Implemented 8 protocols in addition to TH • BS (IEEE Trans. on Information Theory , 1979) • ABS (MobiHoc, 2006) • TW (DIAL-M 2000) • ATW (Tanenbaum, 2002) • STT (Infocom, 2009) • MAS (PerCom, 2007) • ASAP (ICDCS 2010) • Frame Slotted Aloha (IEEE Transactions on Communications, 2005)

  15. Improvement of TH over prior art Uniformly distributed populations • Total number of queries: 50% • Identification time: 10% • Average responses per tag: 30% Non-uniformly distributed populations • Total number of queries: 26% • Identification time: 37% • Average responses per tag: 26%

  16. Normalized Queries

  17. Identification Speed

  18. Normalized Collisions

  19. Normalized Empty Reads

  20. Conclusion First effort towards modeling the Tree Walking process Proposed a method to minimize the expected number of queries More in the paper • Method to make TH reliablein the presence of communication errors • Continuous scanning of dynamically changing tag populations • Multiple readers environment with overlapping regions Comprehensive side-by-side comparison of TH with 8 major prior tag identification protocols

  21. Questions?

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