RFID Topics Temporal Management of RFID Data Mo

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 2 Worcester Polytechnic Institute

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 3

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

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 5 Worcester Polytechnic Institute

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

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

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

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

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='L 003' 10 Worcester Polytechnic Institute

Cont’d • Temporal aggregation of RFID data: find how many items loaded into the store “L 003” on the day of 11/09/2004 SELECT count(epc)FROM OBJECTLOCATION WHERE location_id = 'L 003' AND tstart <= '2004 -11 -09 00: 00. 000' AND tend >= '2004 -11 -09 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' 11 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 Worcester Polytechnic Institute PHYSICAL WORLD TAG 2 Antenna (interface) 2 Controller 2 Application 12

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), Seq(s, ”r 2”); OBSERVATION(“r 2”. OBSERVATION(Ry, e, Ty), OBSERVATION(“R 2”, e, t)Rx<>Ry, -> E. t) -> INSERT: CONTAINMENT(seg(s, “r 2”, Tseq), e, t, e, “UC”) within(Tx, Ty, T) -> DROP: OBSERVATIONS(Rx, Tx) UPDATE: OBJECTLOCATION(e, “L 002”, t, “UC”) Worcester Polytechnic Institute 13

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 14

A Tool to improve query efficiency 15 Worcester Polytechnic Institute

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 16 Worcester Polytechnic Institute

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

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