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RFID Topics

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

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  1. RFID Topics Temporal Management of RFID Data Mo Liu Bart Shappee

  2. OUTLINE • RFID Background • DRER Model • Overview of Syntax • Data Acquisition • Tool for efficiency • Siemens Work Worcester Polytechnic Institute

  3. 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

  4. Cont’d • Integration & Information - What we need to consider: • Time • Location • Being in the physical world • Aggregation Worcester Polytechnic Institute

  5. 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 Worcester Polytechnic Institute

  6. Dynamic Relationship ER Model (DRER) (cont’d) Worcester Polytechnic Institute

  7. cont’d • Static entity tables OBJECT (epc, name, description) SENSOR (sensor_epc, name, description) LOCATION (location_id, name, owner) TRANSACTION (transaction_id, transaction_type) Worcester Polytechnic Institute

  8. cont’d • Dynamic relationship tables OBSERVATION (sensor_epc, value, timestamp) SENSORLOCATION (sensor_epc, location_id, position, tstart, tend) Worcester Polytechnic Institute

  9. OBJECTLOCATION(epc, location id, tstart,tend) CONTAINMENT(epc, parent epc, tstart,tend) TRANSACTIONITEM(transaction_id, epc, timestamp) Worcester Polytechnic Institute

  10. 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' Worcester Polytechnic Institute

  11. 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

  12. 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

  13. 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

  14. 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 Worcester Polytechnic Institute

  15. A Tool to improve query efficiency Worcester Polytechnic Institute

  16. 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 Worcester Polytechnic Institute

  17. Siemens's Product • Middleware • Automatic acquisition and filtering • Have built a working prototype Worcester Polytechnic Institute

  18. Conclusion • Laid a framework for the problems of RFID data acquisition and handling • This paper introduced and pushed the DRER model Worcester Polytechnic Institute

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