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

Muhammad Shahzad

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Radio Frequency Identification

010100110000 1000 11010110 101110101001

Muhammad Shahzad

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Tree Walking (EPCGlobal Standard)

0

00

000 001

01

1

10 11

010 011 100 101

100

0

100

1101

0

101

1Number of queries: 16

1

2

3 4

5

6 7

8

9

10

11 12

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14 15

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

Muhammad Shahzad

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

11

100

0

100

1101

0

101

1

Number of queries: 11 (compared to 16 of TW)

1 2 3 4

11

6 7 9 10

5 8

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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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