1. Adaptive Cleaning for RFID Data Streams VLDB 2006 9/12/06 Shawn Jeffery Minos Garofalakis Michael Franklin UC Berkeley Intel Research Berkeley UC Berkeley

2. Shawn Jeffery HiFi Project UC Berkeley EECS RFID: Radio Frequency IDentification

3. Shawn Jeffery HiFi Project UC Berkeley EECS RFID data is dirty A simple experiment: 2 RFID-enabled shelves 10 static tags 5 mobile tags

4. Shawn Jeffery HiFi Project UC Berkeley EECS RFID Data Cleaning Time Raw readings Smoothed output RFID data has many dropped readings Typically, use a smoothing filter to interpolate SELECT distinct tag_id FROM RFID_stream [RANGE ‘5 sec’] GROUP BY tag_id SELECT distinct tag_id FROM RFID_stream [RANGE ‘5 sec’] GROUP BY tag_id But, how to set the size of the window? But, how to set the size of the window? Smoothing Filter

5. Shawn Jeffery HiFi Project UC Berkeley EECS Window Size for RFID Smoothing Fido movingFido resting Small window Reality Raw readings Large window  Need to balance completeness vs. capturing tag movement

6. Shawn Jeffery HiFi Project UC Berkeley EECS Truly Declarative Smoothing Problem: window size non-declarative Application wants a clean stream of data Window size is how to get it Solution: adapt the window size in response to data

7. Shawn Jeffery HiFi Project UC Berkeley EECS Itinerary Introduction: RFID data cleaning A statistical sampling perspective SMURF Per-tag cleaning Multi-tag cleaning Ongoing work Conclusions

8. Shawn Jeffery HiFi Project UC Berkeley EECS A Statistical Sampling Perspective Key Insight: RFID data  random sample of present tags Map RFID smoothing to a sampling experiment

9. Shawn Jeffery HiFi Project UC Berkeley EECS RFID’s Gory Details EpochTagIDReadRate 01.9 02.6 03.3 Tag 1 Tag 2 Tag 3 Tag 4 Antenna & reader Tags E1E2E3E4E5E6E7E8E9E0 Read Cycle (Epoch) (For Alien readers) Tag List

10. Shawn Jeffery HiFi Project UC Berkeley EECS RFID Smoothing to Sampling RFIDSampling Read cycle (epoch)Sample trial ReadingSingle sample Smoothing windowRepeated trials Read rateProbability of inclusion (p i )  Now use sampling theory to drive adaptation!

11. Shawn Jeffery HiFi Project UC Berkeley EECS SMURF Statistical Smoothing for Unreliable RFID Data Adapts window based on statistical properties Mechanisms for: Per-tag and multi-tag cleaning

12. Shawn Jeffery HiFi Project UC Berkeley EECS Per-Tag Smoothing: Model and Background Use a binomial sampling model Time (epochs) pipi 1 0 Smoothing Window w i Bernoulli trials p i avg SiSi (Read rate of tag i) E1E2E3E4E5E6E7E8E9E0

13. Shawn Jeffery HiFi Project UC Berkeley EECS Per-Tag Smoothing: Completeness If the tag is there, read it with high probability  Want a large window pipi 1 0 Reading with a low p i Expand the window Time (epochs) E1E2E3E4E5E6E7E8E9E0

14. Shawn Jeffery HiFi Project UC Berkeley EECS Per-Tag Smoothing: Completeness Expected epochs needed to read With probability 1-  Desired window size for tag i

15. Shawn Jeffery HiFi Project UC Berkeley EECS Per-Tag Smoothing: Transitions Detect transitions as statistically significant changes in the data pipi 1 0 Statistically significant difference Flag a transition and shrink the window The tag has likely left by this point Time (epochs) E1E2E3E4E5E6E7E8E9E0

16. Shawn Jeffery HiFi Project UC Berkeley EECS Per-Tag Smoothing: Transitions # expected readings Is the difference “statistically significant”? # observed readings

17. Shawn Jeffery HiFi Project UC Berkeley EECS SMURF in Action Fido movingFido resting SMURF  Experiments with real and simulated data show similar results

18. Shawn Jeffery HiFi Project UC Berkeley EECS Multi-tag Cleaning Some applications only need aggregates E.g., count of items on each shelf  Don’t need to track each tag! Use statistical mechanisms for both: Aggregate computation Window adaptation

19. Shawn Jeffery HiFi Project UC Berkeley EECS Aggregate Computation  –estimators (Horvitz-Thompson) Count: P[tag i seen in a window of size w]:  Use small windows to capture movement  Use the estimator to compensate for lost readings

20. Shawn Jeffery HiFi Project UC Berkeley EECS Window Adaptation Upper bound window similar to per-tag “Transition” based on variance within subwindows Count NwNw Nw’Nw’ Time (epochs) E1E2E3E4E5E6E7E8E9E0

21. Shawn Jeffery HiFi Project UC Berkeley EECS Multi-tag Scenario

22. Shawn Jeffery HiFi Project UC Berkeley EECS Ongoing Work: Spatial Smoothing With multiple readers, more complicated Reinforcement  A? B? A U B? A B? Arbitration  A? C?  All are addressed by statistical framework! U A B C D Two rooms, two readers per room

23. Shawn Jeffery HiFi Project UC Berkeley EECS Beyond RFID  -estimator for other aggregates  Use SMURF for sensor networks Use SMURF in general streaming systems (e.g., TelegraphCQ)  Remove RANGE clause from CQL Other sensor data Other streaming data

24. Shawn Jeffery HiFi Project UC Berkeley EECS Related Work Commercial RFID middleware Smoothing filters: need to set smoothing window RFID-related work Rao et al., StreamClean: complementary Intel Seattle, HiFi, ESP: static window size BBQ, MauveDB Heavyweight, model-based SMURF is non-parametric, sampling-based Statistical filters (digital signal processing) Non-linear digital filters inspired SMURF design

25. Shawn Jeffery HiFi Project UC Berkeley EECS Conclusions Current smoothing filters not adequate Not declarative! SMURF: Declarative smoothing filter Uses statistical sampling to adapt window size

26. Shawn Jeffery HiFi Project UC Berkeley EECS Thanks! Questions? jeffery@cs.berkeley.edu