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Adaptive Stream Resource Management Using Kalman Filters

Adaptive Stream Resource Management Using Kalman Filters. Aug 6 2004 UCLA DB seminar. Paradigm. Base station passively wait for sensors update UCSB Stanford (STREAM) U Maryland Brown (Aurora) U Pennsylvania Cornell (Cougar) Base station can actively contact specific sensors

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Adaptive Stream Resource Management Using Kalman Filters

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  1. Adaptive Stream Resource Management Using Kalman Filters Aug 6 2004UCLA DB seminar

  2. Paradigm • Base station passively wait for sensors update • UCSB • Stanford (STREAM) • U Maryland • Brown (Aurora) • U Pennsylvania • Cornell (Cougar) • Base station can actively contact specific sensors • Berkeley (TinyOS / TinyDB) • Brown (Aurora)

  3. Motivation • Reduce communication cost • Reduce power consumption • Reduce bandwidth • Reduce computation cost at base station • Tradeoff : imprecise answer

  4. Basic approach • Base station keep a stale copy of sensors reading • Sensors update only when reading fall out of boundary

  5. Improvement • Sensors readings are predictable • Location of moving objects • power usage • Temperature • Heart-beat rate • Network traffic • Precipitation?

  6. Kalman filter • Prediction of discrete time linear system

  7. Kalman filter I • x – stateu – user inputa – relation between successive statesb – relation between input and state

  8. Kalman filter II • w - noise

  9. Kalman filter III • z – measurementv – measurement noiseh – relation between measurement and state

  10. Kalman filter IV

  11. Kalman filter V

  12. Dual Kalman Filter (DKF) • Base station and sensors maintain the same Kalman filter

  13. Architecture of DKF model

  14. Experiment – moving object

  15. Result – communication cost

  16. Experiment – power load

  17. Result – communication cost

  18. Conclusion • Shift “intelligence” (computation) to sensors • Compressing Historical Information in Sensor Networks A. Deligiannakis, Y. Kotidis, N. Roussopoulos in SIGMOD 2004 • Optimization of Online, In-Network Data ReductionJ. M. Hellerstein, W. Wangin International Workshop on Data Management for Sensor Network 2004

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