Probabilistic Optimal Tree Hoppingfor RFID Identification
Muhammad Shahzad Alex X. LiuDept. of Computer Science and Engineering
Michigan State UniversityEast Lansing, Michigan, 48824, USA
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RFID is everywhere
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Radio Frequency Identification
010100110000 1000 11010110 101110101001
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Tree Walking (EPCGlobal Standard)
0
00
000 001
01
1
10 11
010 011 100 101
100
0
100
1101
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101
1Number of queries: 16
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Optimizing Tree Walking
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Total queries = successful + collisions + empty Minimize total queries
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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
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Our Modeling of Tree Walking
𝑇=∑𝑙=1
𝑏
∑𝑝=0
2𝑙− 1
𝐼 ( 𝑙 ,𝑝)
E [𝑇 ]=∑𝑙=1
𝑏
∑𝑝=0
2𝑙−1
𝑃 {(𝑙 ,𝑝 ) }
equals the probability that parent of node is a collision
𝑃 {¿ tags=𝑘 }=(𝑚𝑘 )(𝑛−𝑚𝑧−𝑘 )
(𝑛𝑧)(Hypergeometric distribution)
Level l
Position p
n=16
m=4
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𝑃𝑐=𝑃 {𝑘>1 }
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Proposed Approach
1. Estimate unidentified tag population size2. Find optimal level and the first unvisited node3. Perform Tree Walking. Go to step 1
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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
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Calculating Optimal Level
E [𝑇 ]=2𝛾+ ∑𝑙=𝛾+1
𝑏
∑𝑝=0
2𝑙
𝑃 {(𝑙 ,𝑝 ) }
Calculate if we start from level between and
Minimize to obtain optimal
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Effect of obtaining optimal
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Tree Hopping vs. Tree Walking
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Tree Hopping Example
000 001 010 011 100 101
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Number of queries: 11 (compared to 16 of TW)
1 2 3 4
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6 7 9 10
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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)
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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%
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Normalized Queries
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Identification Speed
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Normalized Collisions
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Normalized Empty Reads
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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
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Questions?
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