1 / 19

Macro-calibration

Macro-calibration. Kamin Whitehouse David Culler WSNA, September 28 2002. Macro-Calibration. Calibration problems in Sensor Networks Many, many devices noisy devices and environments Post-deployment calibration Macro-calibration Calibrate the network, not the devices

shea-nash
Download Presentation

Macro-calibration

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Macro-calibration Kamin Whitehouse David Culler WSNA, September 28 2002

  2. Macro-Calibration • Calibration problems in Sensor Networks • Many, many devices • noisy devices and environments • Post-deployment calibration • Macro-calibration • Calibrate the network, not the devices • Leverage redundancy to reduce noise • Use the network to calibrate itself

  3. Talk Outline • Example application: distance estimation • Traditional calibration • Iterative calibration • Macro-calibration • Joint calibration • Auto-calibration

  4. Calamari Overview • Simultaneously send sound and RF signal • Time stamp both upon arrival • Subtract • Multiply by speed of sound

  5. No Calibration: 74.6% Error

  6. Sources of Noise in Calamari • Bias – startup time for mic/sounder oscillation • Gain – Volume and sensitivity affect PLL • Frequency -- |FT-FR| affects volume • Orientation – |OT-OR|affects volume

  7. The calibration problem in Calamari • Chicken or egg? • Need sounder to calibrate microphones • Need microphone to calibrate sounders • Note that all calibration problems are really sensor/actuator problems.

  8. Traditional Calibration • Iterative Calibration • Designate one ‘reference’ node • Calibrate all others against it • De facto standard for relative calibration: • The ‘standard meter’ approach • Hightower ’00 used it for localization

  9. Traditional Calibration: 19.7%

  10. Naive Calibration: 21% Error

  11. Traditional Calibration • Weaknesses • Noise propagation • Unobserved parameters

  12. Macro: Joint Calibration • Collect distance estimates for all pairs • Create system of equations ri* = Gtri + Grri + Bt + Br • Choose device parameters that optimize overall system

  13. Joint Calibration: 10.1%

  14. Macro: Joint Calibration • Strengths • Exploits redundancy to reduce noise • Weaknesses • Centralized computation • Cannot handle non-linear parameters

  15. Macro: Auto-Calibration • All transmitter/receiver pairs are also receiver/transmitter pairs • These symmetric edges should be equal • Let dTR =BT + BR + GT*r + GR*r For all transmitter/receiver pairs i, k: dik = dki

  16. Macro: Auto-Calibration • All distances in the network must follow the triangle inequality • Let dTR =BT + BR + GT*r + GR*r For all connected nodes h, i, k: dih + dik - dhk >=0

  17. Consistency/constraint-based • Choose parameters that maximize consistency while satisfying all constraints • A quadratic program arises Minimize: Σik(dik –dki)2 + ΣT(GT–1)2 + ΣR(GR–1)2 Subject to: dih + djk - dhk >=0 for all trianglehik

  18. Future Work • Non-gaussian variations of the above algorithms • Non-linear parameter estimation • Expectation\maximization • MCMC

  19. Conclusions • Macro-calibration • Easier and faster • Allows global optimization • Leverages redundancy • Dependencies between sensors

More Related