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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 - PowerPoint PPT Presentation
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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
Worcester Polytechnic Institute
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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
Worcester Polytechnic Institute
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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'
Worcester Polytechnic Institute
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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