RFID Data Aggregation

Dritan Bleco, Yannis KotidisDepartment of Informatics

Athens University of Economics and Business

Yannis Kotidis

Outline

Introduction Temporal Aggregation

Basic Temporal Aggregation - BTA Lossy Temporal Aggregation - LTA

Spatial Aggregation Evaluation Conclusions

Yannis Kotidis

Radio Frequency Identification (RFID) Use radio-frequency waves to transfer data

between a reader and a movable item to identify, categorize, track...

Does not require physical sight or contact between reader/scanner and the tagged item

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Variations

Active/Passive Tags Memory Size (16bits- KBs) Memory Type

Read Only, WORM, Read/Write Frequency

125KHz - 5.8 GHz Physical Dimensions

Thumbnail to Brick sizes Price (few cents-hundred euros)

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Existing Applications

Animal/livestock tracking Postal services (routing and

sorting) Libraries Toll collection Warehousing Supply chain management …

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RFID System Architecture

RFID Reader

tag

RFID ReaderRFID Reader

tagtag

tag tagtagtag

tag

Edgeware (on-site)filtering, cleaning, aggregation

Raw RFID Data Stream

Aggregated RFID Data Stream

Middleware (remote)

IT Applications

tagLevel-0

Level-1

Level-2

Level-3

EPC Code (tag)

Time (reader)

Location (reader)

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Simple RFID data stream model

Assume streaming records with schema EPC code: EPCi

(Discrete) Time: ti

Location: loci

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Basic Temporal Aggregation (BTA)

Collate consecutive reports of the same tag

Reader(EPCi,loci,tstart)

(EPCi,loci,tstart+1)

(EPCi,loci,tstart+2)

…

(EPCi,loci,tend)

tstart tend

Raw stream

(EPCi,loci,tstart,tend)

Aggregated stream

EPCi

loci

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Problems with BTA

RFID readers often drop observations e.g. due to collisions Up to 30% loss is not uncommon [Jeffery2006]

Objects are often moved within the facility Multiple BTA records

Reduction depends on data characteristics Need an application-controllable reduction

framework OLAP analysis does not require precise

knowledge!

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Lossy Temporal Aggregation (LTA)

LTA record format: (EPC,loc,tstart,tend,p) Tag may be partially present during the

interval Value denotes the fraction of times that the

tag was observed during the interval BTA: p=1 (implied) LTA: 0<p≤1

Allow us to control the size of the aggregated stream or the level of accuracy

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Types of Error in LTA

tstart tend

X=epochs when tag was reported in [tstart,tend]

Y=epochs when tag was not reported in [tstart,tend]

p = X / (X+Y)

Tag spotted but not reported

Tag spotted but reported with

probability p instead of 1

Tag not spotted but nevertheless

reported with probability p

selected LTA interval

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Problem Formulation

Compute best B-tuple LTA representation such that cumulative error (including both false negative and false positive error types) is minimized Cumulative Error = 2*X*Y/(X+Y)2

Other error metrics? Dual problem also interesting

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Helpful Observations

1. Selected end-points tstart,tend must contain appearance of a tag

2. Should not break consecutive observations

Bad choice due to (1)

Bad choice due to (2)

Thus, we can first apply BTA and afterwards LTA

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Linear Algorithm

Goal: generate B LTA records Input: n BTA records

Example: Reduce stream from 8 to B=4 records

BTA Interval

LTA Interval

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Greedy Algorithm

Iteratively merge intervals Select best candidate at each step Stop when left with exactly B intervals

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Optimal LTA

Dynamic Programming formulation E(i,k): error of best k LTA representation for

first i BTA intervals err(a,b): error for single LTA record encoding

intervals a, a+1, … b

k-1 LTA intervals

1 2 j j+1 i

1 LTA interval

E(i,k)=min(E(j,k-1)+err(j+1,i))j<i

BTA intervals:

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Spatial Aggregation

Tags often move in batches Common in

supply-chain/distribution networks

Idea: create surrogate EPC codes to replace multiple tags packaged together Proposed in [Gonzales et all

ICDE 2006] Note:

Do not know in advance how items are grouped

Surrogate codes do not imply physical grouping

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Example

G1: I1 I2

Surrogate Group codes

I1 L1 T1 T5 .78

I2 L1 T1 T5 .69

I3 L1 T2 T5 .90

I1 L2 T12 T22 .67

I2 L2 T12 T22 .62

I4 L2 T12 T22 .66

LTA stream

These items are observed at

the same interval/location

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Example

G1L1 T1 T5 .69

I3 L1 T2 T5 .90

I1 L2 T12 T22 .67

I2 L2 T12 T22 .62

I4 L2 T12 T22 .66

LTA stream

New record replaces both entries

G1: I1 I2

G2: G1 I4

Surrogate Group codes

More tags

spotted together

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Resulting Tables

G1L1 T1 T5 .69

I3 L1 T2 T5 .90

G2 L2 T12 T22 .62

Reduced stream

G1: I1 I2

G2: G1 I4

Surrogate Group codes

I1 L1 T1 T5 .78

I2 L1 T1 T5 .69

I3 L1 T2 T5 .90

I1 L2 T12 T22 .67

I2 L2 T12 T22 .62

I4 L2 T12 T22 .66

LTA stream

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Experiments

Used RFID traces from 2008 Hope Conference in New York Sampled data at 30sec intervals 1.9Million records Reduced to 423K records via BTA

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Accuracy (LTA)

Picked tag with most intervals (569) Vary number of requested LTA-tuples (B)

0

500

1000

1500

2000

0 200 400 600

Output Intervals (B)

Cu

mu

lati

ve

Err

or

OptimalDPGreedyLinear

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Execution Times (LTA)

1

10

100

1000

10000

100000

1000000

0 200 400 600

Output Intervals (B)

Ex

ec

uti

on

Tim

e (

ms

ec

s)

OptimalDPGreedyLinear

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Notes on Spatial Aggregation

Input: 434K BTA records Output

77K surrogate group ids 39% space reduction (accounting surrogates) 3.3secs (1.83GHz Core Duo with 1GB)

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Different Choices

0

5

10

15

20

25

Initial Stream Basic Temp Aggr Basic Temp Aggr+Spatial Aggr Greedy Greedy+Spatial Aggr

Str

eam

Siz

e (M

B)

Lossless Lossy

3:13:1

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Conclusions

Explored different aggregation schemes Exploit temporal and spatial correlations

Schemes reduce size of RFID stream in a user-controllable manner

All algorithms are fairly fast Greedy is orders of magnitude faster than OptimalDP

with practically identical performance More schemes possible

Ex: spatial with fuzzy groups Other error metrics, dual problem

Yannis Kotidis

Thank you,

Questions?