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RFID Topics. Temporal Management of RFID Data. Mo Liu Bart Shappee. OUTLINE. RFID Background DRER Model Overview of Syntax Data Acquisition Tool for efficiency Siemens Work. RFID - Background. Radio Frequency Identification Major Characteristics: Streaming Data

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rfid topics

RFID Topics

Temporal Management of RFID Data

Mo Liu

Bart Shappee

outline
OUTLINE
  • RFID Background
  • DRER Model
  • Overview of Syntax
  • Data Acquisition
  • Tool for efficiency
  • Siemens Work

Worcester Polytechnic Institute

rfid background
RFID - Background
  • Radio Frequency Identification
  • Major Characteristics:
    • Streaming Data
      • Temporal and Dynamic
    • Unreliable Data
      • Mainly Missed Reads & Duplicates
    • Very Large Volume of Information
    • Integration
      • RFID Data needs to be handled by existing applications

Worcester Polytechnic Institute

cont d
Cont’d
  • Integration & Information - What we need to consider:
    • Time
    • Location
      • Being in the physical world
    • Aggregation

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dynamic relationship er model drer
Dynamic Relationship ER Model (DRER)
  • RFID entities are static and are not altered in the businessprocesses
  • RFID relationships: dynamic and change all the time
  • Dynamic Relationship ER Model

– Simple extension of ER model

Two types of dynamic relationships added:

– Event-based dynamic relationship. A timestamp attribute added torepresent the occurring timestamp of the event

– State-based dynamic relationship. tstart and tend attributes addedto represent the lifespan of a state

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cont d1
cont’d
  • Static entity tables

OBJECT (epc, name, description)

SENSOR (sensor_epc, name, description)

LOCATION (location_id, name, owner)

TRANSACTION (transaction_id, transaction_type)

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cont d2
cont’d
  • Dynamic relationship tables

OBSERVATION (sensor_epc, value, timestamp)

SENSORLOCATION (sensor_epc, location_id, position, tstart, tend)

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slide9
OBJECTLOCATION(epc, location id, tstart,tend)

CONTAINMENT(epc, parent epc, tstart,tend)

TRANSACTIONITEM(transaction_id, epc, timestamp)

Worcester Polytechnic Institute

tracking and monitoring rfid data
Tracking and Monitoring RFID Data
  • RFID object tracking:find the location history of object“EPC”

SELECT * FROM OBJECTLOCATION WHERE epc=\'EPC‘

Missing RFID object detection: find when and where object“mepc” was lost

SELECT location_id, tstart, tend

FROM OBJECTLOCATION

WHERE epc=\'mepc\' and tstart =(SELECT MAX(o.tstart)

FROM OBJECTLOCATION o

WHERE o.epc=‘mepc\')

  • RFID object identification: a customer returns a product

“XEPC”. Check if the product was sold from this store

SELECT * FROM OBJECTLOCATION

WHERE epc=\'XEPC\' AND location_id=\'L003\'

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cont d3
Cont’d
  • Temporal aggregation of RFID data: find how many itemsloaded into the store “L003” on the day of 11/09/2004

SELECT count(epc)FROM OBJECTLOCATION

WHERE location_id = \'L003\'

AND tstart <= \'2004-11-09 00:00:00.000\'

AND tend >= \'2004-11-09 00:00:00.000‘

  • RFID Data Monitoring—monitor the states of RFID objects

RFID object snapshot query: find the direct container of object “EPC” at time T

SELECT parent_epc FROM CONTAINMENT

WHERE epc=\'EPC\' AND tstart <= \'T\' AND tend >= \'T\'

Worcester Polytechnic Institute

rfid data acquisition
Data is automatically generated from the physical world through Readers and Tags

Modes if Acquisition

Full/Half Duplex

Sequential Mode

This information includes EPCs and timestamps

Other stored values may also be transmitted

PHYSICAL WORLD

TAG

2

Antenna (interface)

2

Controller

2

Application

RFID - Data Acquisition

Worcester Polytechnic Institute

rfid data acquisition part 2
RFID - DATA Acquisition Part 2

How do we improve on this?

Data is also pre-porocessed

  • Data Filtering
  • Local Transformation
  • Data Aggregation

OBSERVATION(Rx, e, Tx),

OBSERVATION(Ry, e, Ty), Rx<>Ry,

within(Tx, Ty, T) -> DROP:OBSERVATIONS(Rx, e, Tx)

Seq(s,”r2”);OBSERVATION(“r2”. E. t) ->

INSERT:CONTAINMENT(seg(s, “r2”, Tseq), e, t, “UC”)

OBSERVATION(“R2”, e, t) ->

UPDATE:OBJECTLOCATION(e, “L002”, t, “UC”)

Worcester Polytechnic Institute

rfid data acquisition part 3
RFID - DATA Acquisition Part 3

How do we improve on this (even more)?

Data is also handled with rules

some examples are:

  • Sate Modification (i.e. time at toll)
    • Creation
    • Deletion
  • Containment (1000 ipods in a case)
    • Change location of the 1000 ipods

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a tool to improve query efficiency
A Tool to improve query efficiency

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data partitioning
Increase of data volumes slows down queries

Data have a limited active cycle

– Non-active objects can be periodicallyarchived into historysegments

– Active segments with a high active object ratio is used for updates

This partition technique assures efficient update andqueries

Data Partitioning

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siemens s product
Siemens\'s Product
  • Middleware
    • Automatic acquisition and filtering
    • Have built a working prototype

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conclusion
Conclusion
  • Laid a framework for the problems of RFID data acquisition and handling
  • This paper introduced and pushed the DRER model

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