Adaptive Cleaning for Adaptive Cleaning for RFID Data StreamsRFID Data Streams

Presented by Willie and Abhishek

Shawn Jeffery Minos Garofalakis Michael Franklin UC Berkeley Intel Research Berkeley UC Berkeley

Disclaimer: The slides are taken from Jeffrey’s talk at VLDB ‘06

04/22/23

RFID: Radio Frequency RFID: Radio Frequency IDentificationIDentification

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RFID data is dirtyRFID data is dirtyShelf 0 Shelf 1

RFIDReaders

StaticTags

Mobile Tags

15ft1.5ft

3ft9ft

3ft

3ft

3ft

A simple experiment:•2 RFID-enabled shelves•10 static tags•5 mobile tags

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RFID Data CleaningRFID Data Cleaning

Time

Raw readings

Smoothed output

• RFID data has many dropped readings• Typically, use a smoothing filter to

interpolateSELECT distinct tag_idFROM RFID_stream [RANGE ‘5 sec’]GROUP BY tag_idBut, how to set the size

of the window?

Smoothing Filter

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Window Size for RFID Window Size for RFID SmoothingSmoothing

Fido moving Fido resting

Small windowRealityRaw readings

Large window

Need to balance completeness vs. capturing tag movement

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Truly Declarative Truly Declarative SmoothingSmoothing

• 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

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ItineraryItinerary

• Introduction: RFID data cleaning• A statistical sampling perspective• SMURF

• Per-tag cleaning• Multi-tag cleaning

• Ongoing work• Conclusions

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A Statistical Sampling A Statistical Sampling PerspectivePerspective

• Key Insight: RFID data random sample of present tags

• Map RFID smoothing to a sampling experiment

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RFID’s Gory DetailsRFID’s Gory Details

Epoch TagID ReadRate0 1 .90 2 .60 3 .3

Tag 1

Tag 2

Tag 3

Tag 4

Antenna & readerTags

E1 E2 E3 E4 E5 E6 E7 E8 E9E0

Read Cycle (Epoch)

(For Alien readers)

Tag List

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RFID Smoothing to SamplingRFID Smoothing to Sampling

RFID SamplingRead cycle (epoch) Sample trialReading Single sampleSmoothing window Repeated trialsRead rate Probability of inclusion

(pi)

Now use sampling theory to drive adaptation!

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SMURFSMURF• Statistical Smoothing for Unreliable RFID

Data• Adapts window based on statistical

properties• Mechanisms for:

• Per-tag and multi-tag cleaning

Multi-tagCleaning

SMURFPer-tag

Cleaning

raw RFID streams

cleanedcount readings

cleanedper-tag readings

Application(s) Application(s)

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Per-Tag Smoothing: Per-Tag Smoothing: Model and BackgroundModel and Background

• Use a binomial sampling model

Time (epochs)

pi

1

0

Smoothing Window

wi Bernoulli trials

piavg

Si

(Read rate of tag i)

E1 E2 E3 E4 E5 E6 E7 E8 E9E0

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Per-Tag Smoothing: Per-Tag Smoothing: CompletenessCompleteness

• If the tag is there, read it with high probability

Want a large window

pi

1

0

Reading with a low pi

Expand the window

Time (epochs)E1 E2 E3 E4 E5 E6 E7 E8 E9E0

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Per-Tag Smoothing: Per-Tag Smoothing: CompletenessCompleteness

Expected epochs needed to read

With probability 1-

Desired window size for tag i

1ln*1

avgi

ip

w

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Per-Tag Smoothing: Per-Tag Smoothing: TransitionsTransitions• Detect transitions as statistically

significant changes in the data

pi

1

0

Statistically significant difference Flag a transition and

shrink the window

The tag has likely left by this point

Time (epochs)E1 E2 E3 E4 E5 E6 E7 E8 E9E0

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Per-Tag Smoothing: Per-Tag Smoothing: TransitionsTransitions

# expected readings Is the difference

“statistically significant”?# observed

readings

)1(**2|*||| avgi

avgii

avgiii ppwpwS

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SMURF in ActionSMURF in ActionFido moving Fido resting

SMURF

Experiments with real and simulated data show similar results

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Multi-tag CleaningMulti-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

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Aggregate Aggregate ComputationComputation

• –estimators (Horvitz-Thompson) • Count:

• P[tag i seen in a window of size w]:

Use small windows to capture movementUse the estimator to compensate for lost

readings

wSiwN

1

wavgii p )1(1

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Window AdaptationWindow Adaptation

• Upper bound window similar to per-tag

• “Transition” based on variance within subwindows

1ln*1

avgpw

CountNw

Nw’

Time (epochs)E1 E2 E3 E4 E5 E6 E7 E8 E9E0

'VarVar2ww NN

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Multi-tag ScenarioMulti-tag Scenario

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Ongoing Work: Spatial Ongoing Work: Spatial SmoothingSmoothing

• 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

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Beyond RFIDBeyond 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

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Related WorkRelated 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

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ConclusionsConclusions

• Current smoothing filters not adequate• Not declarative!

• SMURF: Declarative smoothing filter• Uses statistical sampling to adapt window size

04/22/23

Thanks!Thanks!

Questions?