Download presentation
Presentation is loading. Please wait.
1
Adaptive Stream Resource Management Using Kalman Filters Aug 6 2004 UCLA 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 – state u – user input a – relation between successive states b – relation between input and state
8
Kalman filter II w - noise
9
Kalman filter III z – measurement v – measurement noise h – 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 Reduction J. M. Hellerstein, W. Wang in International Workshop on Data Management for Sensor Network 2004
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.