1. 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

2. 2 RFID is everywhere Muhammad Shahzad

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

4. 4 Tree Walking (EPCGlobal Standard) 0 00 000 001 01 1 10 11 010 011 100101 1000100110101011 Number of queries: 16 1 2 3 4 5 67 8 9 10 11 12 13 1415 16 Muhammad Shahzad

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

6. 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 Muhammad Shahzad

7. 7 Our Modeling of Tree Walking (Hypergeometric distribution) Level l Position p n=16 m=4 Muhammad Shahzad

8. 8 Proposed Approach 1.Estimate unidentified tag population size 2.Find optimal level and the first unvisited node 3.Perform Tree Walking. Go to step 1 Muhammad Shahzad

9. 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 Muhammad Shahzad

10. 10 Calculating Optimal Level Muhammad Shahzad

11. 11 Muhammad Shahzad

12. 12 Tree Hopping vs. Tree Walking Muhammad Shahzad

13. 13 Tree Hopping Example 000001 010011100101 11 1000100110101011 Number of queries: 11 (compared to 16 of TW) 1 2 3 4 11 6 7910 5 8 Muhammad Shahzad

14. 14 Experimental Evaluation  Implemented 8 protocols in addition to TH 1.BS (IEEE Trans. on Information Theory, 1979) 2.ABS (MobiHoc, 2006) 3.TW (DIAL-M 2000) 4.ATW (Tanenbaum, 2002) 5.STT (Infocom, 2009) 6.MAS (PerCom, 2007) 7.ASAP (ICDCS 2010) 8.Frame Slotted Aloha (IEEE Transactions on Communications, 2005) Muhammad Shahzad

15. 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% Muhammad Shahzad

16. 16 Normalized Queries Muhammad Shahzad

17. 17 Identification Speed Muhammad Shahzad

18. 18 Normalized Collisions Muhammad Shahzad

19. 19 Normalized Empty Reads Muhammad Shahzad

20. 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 reliable in 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 Muhammad Shahzad

21. 21 Questions? Muhammad Shahzad