AdaptiveRFID Hamid

8/15/2019 AdaptiveRFID Hamid

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Adaptive Cleaning forAdaptive Cleaning for

RFID Data StreamsRFID Data Streams

Shawn Jeffery Minos Garofalakis Michael Franklin

C !erkeley Intel Research !erkeley C !erkeley

"resented #y$ %amid %aidarian Shahri


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Where Are We? Look at the Signs!Where Are We? Look at the Signs!


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Looking at Signs – Before Jumping InLooking at Signs – Before Jumping In

& S. Chaudhuri, U. aa", #An $%er%ie& ofata Warehousing and $LA' (e)hno"og,#SIGMOD Record , *++. -/ )itations

& W and information integration

& 0ata )"eaning1 term pu2"i)i3ed Identified its importan)e in integration

& 45tensi%e resear)h fo""o&ed


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6LB 7*6LB 7*

& Session 8*79 A(A :UALI(; < CL4A=I=>

& e)"arati%e data )"eaning9 "anguage, mode", anda"gorithms

Helena Galhardas (INRIA Rocquencourt), Daniela Florescu(Propel), Dennis Shasha (N!), "ric Si#on, and $ristian% Au&ustin Saita (INRIA Rocquencourt)

& 'otters &hee"9 an intera)ti%e data )"eaning sstem'iashan*ar Ra#an and +oseph M Hellerstein(!ni-ersit o. $ali.ornia at /er*ele)

& Update propagation strategies for impro%ing the @ua"it ofdata on the We2

Ale0andros 1a2rinidis and Nic* Roussopoulos (!ni-ersito. Marland)


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ata C"eaning 're%ious Work 7ata C"eaning 're%ious Work 7

& amid aidarian Shahri, S.. Shahri, 04"iminating up"i)ates inInformation Integration9 An Adapti%e, 45tensi2"e Drame&ork,# I"""Intelli&ent Sste#s, 6o". 7*, =o. E, 7.


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'utting (hings into Conte5t'utting (hings into Conte5t

& ata )"eaning re@uired after integration

=o unified standard a)ross sour)es

=$W9 sensorFhard&are errors ine%ita2"eG

resear)h opportunit

& ata mode"ing HAmo" eshpande

An i#portant use )ase is )"eaning


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6LB 7 – (hree &eeks ago6LB 7 – (hree &eeks ago

& 8esear)h Session E9 Sensor ata Hdedi)ated to )"eaning!

& (it"e9 Adapti%e C"eaning for 8DI ata Streams

Authors9 Sha&n 8. Jeffer, Kinos >arofa"akis, Ki)hae" J.Drank"in

& (it"e9 A eferred C"eansing Kethod for 8DI ata Ana"ti)s

Authors9 Jun 8ao, Sangeeta orais&am, eta" (hakkar, Latha S.Co"2

& (it"e9 $n"ine $ut"ier ete)tion in Sensor ata Using =on'arametri) Kode"s

Authors9 Sharmi"a Su2ramaniam, (hemis 'a"pana, imitris'apadopou"os, 6ana a"ogeraki, imitrios >unopu"os


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8DI9 8adio Dre@uen) Ientifi)ation8DI9 8adio Dre@uen) Ientifi)ation


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8DI data is dirt8DI data is dirt

A simple experiment:

•2 RFID-enabledshelves

•10 static tags•5 mobile tags


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8DI ata C"eaning8DI ata C"eaning

Time

Rawreadings

Smoothedoutput

& 8DI data has man dropped readings& (pi)a"", use a s#oothin& .ilter to interpo"ate

S!"# distinct tag$id

FR%& RFID$stream 'RA() *5 sec+,)R%. / tag$id

S!"# distinct tag$idFR%& RFID$stream 'RA() *5 sec+,)R%. / tag$id

/t how to set the si3e

o4 the indo6

/t how to set the si3e

o4 the indo6

Smoothing Filter


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Windo& Si3e for 8DI SmoothingWindo& Si3e for 8DI Smoothing

Fido moving Fido resting

Small windowSmall window

RealityRealityRaw readingsRaw readings

Large windowLarge window

Need to balance completeness vs.capturing tag movement

Need to balance completeness vs.capturing tag movement


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(ru" e)"arati%e Smoothing(ru" e)"arati%e Smoothing

& 'ro2"em9 &indo& si3e nonde)"arati%e App"i)ation &ants a )"ean stream of data

Windo& si3e is ho3 to get it

& So"ution9 adapt the &indo& si3e inresponse to data


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ItinerarItinerar

& Introdu)tion9 8DI data )"eaning

& A statisti)a" samp"ing perspe)ti%e

& SKU8D 'ertag )"eaning

Ku"titag )"eaning

& $ngoing &ork& Con)"usions


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A Statisti)a" Samp"ing 'erspe)ti%eA Statisti)a" Samp"ing 'erspe)ti%e

& 4e Insi&ht98DI data ≈

random samp"e of present tags

& Kap 8DI smoothing to a samp"inge5periment


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8DIMs >or etai"s8DIMs >or etai"s

Epoch TagID ReadRate

0 1 .9

0 2 .6

0 3 .3

#ag 1

#ag 2

#ag 7

#ag 8

Antenna & readerTags

! " # $ % ' ( )*

Read "9cleEpoch;

+For Alien readers,

Tag List


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8DI Smoothing to Samp"ing8DI Smoothing to Samp"ing

8DI Samp"ing

8ead ))"e Hepoch Samp"e tria"

8eading Sing"e samp"e

Smoothing &indo& 8epeated tria"s

8ead rate 'ro2a2i"it of in)"usion Hpi

(o se sampling theor9 to drive adaptation<


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SKU8DSKU8D

& Statistical S#oothing for ! nreliable RF ID Data& Adapts window #ased on statistical properties& Mechanisms for$

& "er'tag and m(lti'tag cleaning


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'er(ag Smoothing9 Kode" and'er(ag Smoothing9 Kode" and

Ba)kgroundBa)kground

& Use a 2inomia" samp"ing mode"

Time

+epochs,

pi

!

*

Smoothing -indow

wi ernoulli trials

piavg

Si

+Read rateo/ tag i,

! " # $ % ' ( )*


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'er(ag Smoothing9 Comp"eteness'er(ag Smoothing9 Comp"eteness

& If the tag is there, read it &ith high pro2a2i"it Want a "arge &indo&

pi

!

*

Reading with alow pi

0pand the window

Time+epochs,! " # $ % ' ( )*


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'er(ag Smoothing9 Comp"eteness'er(ag Smoothing9 Comp"eteness

0pected epochs

needed to read

-ith probability

!1

2esired windowsi3e /or tag i

= δ

1ln*

1

avg i

i

pw


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'er(ag Smoothing9 (ransitions'er(ag Smoothing9 (ransitions

& ete)t transitions as statisti)a""signifi)ant )hanges in the data

pi

!

*

Statistically signi/icant di//erence

Flag a transition andshrin4 the window

The tag has li4ely le/t bythis point

Time+epochs,! " # $ % ' ( )*


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'er(ag Smoothing9 (ransitions'er(ag Smoothing9 (ransitions

5 e0pectedreadings

6s the di//erence 7statistically signi/icant89

5 observedreadings

)1(**2|*||| avg

i

avg

ii

avg

iii p pw pwS −>−

&Statisti)a"" signifi)antStatisti)a"" signifi)ant


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SKU8D in A)tionSKU8D in A)tion

Fido moving Fido resting

S:;RFS:;RF

0periments with real and simulated datashow similar results


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Ku"titag C"eaningKu"titag C"eaning

& Some app"i)ations on" need aggregates

4.g., )ount of items on ea)h she"f onMt need to tra)k ea)h tag!

& Use statisti)a" me)hanisms for 2oth5 Aggregate )omputation

Windo& adaptation


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Aggregate ComputationAggregate Computation

& –estimators Hor%it3(hompson

& Count9

& 'Ntag i seen in a &indo& of si3e &O9

Use sma"" &indo&s to )apture mo%ement

Use the estimator to )ompensate for "ost readings

∑ ∈∧

=wS iw N

ι

π

1

wavg

ii p )1(1 −−=π


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Windo& AdaptationWindo& Adaptation

& Upper 2ound &indo& simi"ar to pertag

& 0(ransition1 2ased on %arian)e &ithin

su2&indo&s

=

δ

1ln*

1avg

p

w

"ont

(

(+

Time+epochs,! " # $ % ' ( )*

+

>

∧∧

'Var Var 2ww

N N


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Ku"titag S)enarioKu"titag S)enario


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$ngoing Work9 Spatia" Smoothing$ngoing Work9 Spatia" Smoothing

& With mu"tip"e readers, more )omp"i)ated

Reinforcement

A9 9 A ; 9 A 9

Arbitration

A9 two readers per room


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Beond 8DIBeond 8DI

& estimator for other aggregates

Use SKU8D for sensor net&orks

& Use SKU8D in genera" streaming sstemsHe.g., (e"egraphC:

8emo%e RANGE )"ause from C:L

%ther sensor data%ther sensor data

%ther streaming data%ther streaming data


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8e"ated Work8e"ated Work

& Commer)ia" 8DI midd"e&are Smoothing fi"ters9 need to set smoothing &indo&

& 8DIre"ated &ork

8ao et a"., StreamC"ean9 )omp"ementar Inte" Seatt"e, iDi, 4S'9 stati) &indo& si3e

& BB:, Kau%eB ea%&eight, mode"2ased

SKU8D is nonparametri), samp"ing2ased

& Statisti)a" fi"ters Hdigita" signa" pro)essing < B =on"inear digita" fi"ters inspired SKU8D design


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Con)"usionsCon)"usions

•


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(hanks!(hanks!

:uestions?

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AdaptiveRFID Hamid