rfid topics mo liu bart shappee temporal management of rfid data

RFID Topics

Mo LiuBart Shappee

Temporal Management of RFID Data

Worcester Polytechnic Institute

2

OUTLINE

• RFID Background

• DRER Model

• Overview of Syntax

• Data Acquisition

• Tool for efficiency

• Siemens Work


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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


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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)

• RFID entities are static and are not altered in the business processes

• 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 to represent the occurring timestamp of the event

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


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Dynamic Relationship ER Model (DRER) (cont’d)


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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’d• Dynamic relationship tables

OBSERVATION (sensor_epc, value, timestamp)

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


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OBJECTLOCATION(epc, location id, tstart,tend)

CONTAINMENT(epc, parent epc, tstart,tend)

TRANSACTIONITEM (transaction_id, epc, timestamp)


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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, tendFROM OBJECTLOCATIONWHERE 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 OBJECTLOCATIONWHERE epc='XEPC' AND location_id='L003'


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Cont’d

• Temporal aggregation of RFID data: find how many items loaded 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'


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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


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RFID - DATA Acquisition Part 2

Data is also pre-porocessed

• Data Filtering

• Local Transformation

• Data Aggregation

How do we improve on this?

OBSERVATION(Rx, e, Tx),

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

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

OBSERVATION(“R2”, e, t) ->

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

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

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


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RFID - DATA Acquisition Part 3

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

How do we improve on this (even more)?


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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 periodically archived into history segments

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

• This partition technique assures efficient update and queries


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Siemens's Product

• Middleware– Automatic acquisition and filtering– Have built a working prototype


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Conclusion

• Laid a framework for the problems of RFID data acquisition and handling

• This paper introduced and pushed the DRER model

rfid topics mo liu bart shappee temporal management of rfid data

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