Every Bit Counts – Fast and Scalable RFID Estimation

1. Every Bit Counts – Fast and Scalable RFID Estimation Muhammad Shahzad and Alex X. Liu Dept. of Computer Science and Engineering Michigan State University East Lansing, Michigan, 48824 USA

2. Radio Frequency Identification Chip Passive Active Antenna

3. Radio Frequency Identification

4. RFID Estimation Exact IDs can not be read due to privacy requirements Exact IDs are not required but only a count Identification protocols can use the count to speed up identification process

5. Problem Statement • Input • Confidence interval β∈ (0,1] • Required Reliability α ∈ [0,1) • Output • An estimate te of tag population size t such that • 1-β≤te/ t ≤1+β • P{ 1-β≤te/ t ≤1+β} ≥ α

6. Additional Requirements • Single Reader environment • Multiple reader environment with overlapping regions • C1G2 standard compliant tags • Active tags and Passive tags • Scalable

7. Why do we need a new protocol? • Non compliance with C1G2 standard • Non-scalable • Inability to achieve required reliability • Room for improvement in speed

8. Communication Protocol Overview • Faster to distinguish between empty and non-empty slots • Slower to distinguish between empty, singleton, and collision • Singleton and collision » non-empty • At the end of frame, reader gets a sequence of 0s and 1s • 011C011 becomes 0111011 1 2 3 4 5 6 7 0 1 1 C 0 1 1 0 1 1 C 0 1 1 3 2 6 4 7 4 frame size f =7

9. Estimation • Any measure which is a monotonous function of t can be used for estimation • Number of 1s in a frame • Number of 0s in a frame • Any measure which is a monotonous function of t can be used for estimation • Number of runs of 1s • Number of runs of 0s • Any measure which is a monotonous function of t can be used for estimation • Average run size of 1s • Average run size of 0s • 011100 • 0 • 111 • 00 Mobicom 2012

10. Useable Measures • Number of 1s • Number of 0s • Number of runs of 1s • Number of runs of 0s • Average run size of 1s • Average run size of 0s • Average run size of 1s

11. ART Protocol 1 2 3 4 5 6 7 0 1 1 1 0 1 1 Calculate avg. run size of 1s from n frames 0 1 1 1 0 1 1 Obtain the estimate frame size f = 7 3 2 6 4 7 4 Repeat frames n times

12. Scalability Problem 0 1 C C 1 C C C 0 1 C C 0 1 C

13. Scalability Problem Addressed Use persistence probability p = 0.25 Obtain the estimate using information from this frame Tags follow a uniform distribution Extrapolate with the factor of p 0 1 C 0 8 3 5 16 11 12 frame size f = 4/p = 16 2 5 3 12 9 7

14. Optimization • Estimation time ∝ f × n • d/df (f ×n ) = 0 • The expression for number of rounds n depends on • Confidence interval β • Required Reliability α • Frame size f • n =func(α, β, f ) • Two equations • n = func(α, β, f ) • d/df (f ×n ) = 0 • Two unknowns • Number of rounds n • Frame size f

15. Multiple Readers Environment • First proposed by Kodialam et. al. in “Anonymous tracking using RFID tags” Seed R Seed R 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 0 0 0 Logical OR 3 2 1 f =4 R f =4 R 1 2 frame size f = 4

16. Advantages of ART over prior art • Speed: • 7 times faster than fastest • β= 0.1%, α = 99.9% • Deployability • Does NOT require modifications to • tags • communication protocol

17. Performance Evaluation

18. Performance Evaluation

19. Performance Evaluation

20. Conclusion • New estimator: the average run size of 1s • Faster than existing estimation schemes • smaller variance • Single and multiple reader environment • C1G2 standard compliant tags • Active tags and Passive tags • Scalable: independent of tag population size

21. Questions?