1 / 9

Macro-Calibration in Sensor/Actuator Networks

Macro-Calibration in Sensor/Actuator Networks. Kamin Whitehouse, David Culler . Problem. Individual sensor calibration in a large sensor network is not feasible Sensors difficult to calibrate because of a lack of a calibration interface . Calibration Techniques. Traditional calibration

hermione-wright
Download Presentation

Macro-Calibration in Sensor/Actuator Networks

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 in Sensor/Actuator Networks Kamin Whitehouse, David Culler

  2. Problem • Individual sensor calibration in a large sensor network is not feasible • Sensors difficult to calibrate because of a lack of a calibration interface

  3. Calibration Techniques • Traditional calibration • Parameters belong to transmitter/receiver pair • Iterative calibration • Calibrate receivers against a reference transmitter • Suffers from error propagation • Assumes reference is calibrated well, error is in uncalibrated device • Mean calibration • Calibrate receivers against all transmitters • Does not allow calibrate both receivers and transmitters • Avoids separation problem by not calibrating transmitters • Separation Problem • Inability to distinguish receiver effects from transmitter effects

  4. Joint Calibration • Looks at system as a whole • Technique • Parameterize each device and model system response • Collect data from system • Choose parameters such that system performance is optimized • Previous techniques optimize individual device response • Only need to use system output to calibrate – don’t need to directly observe actual response. • Ex. In localization, only need distance estimates

  5. Calimari • MICA motes • Uses RSSI and acoustic TOF ranging

  6. Experiments • 32 MICA motes in grid on table 210cm x 90cm • Each node generated up to 31 distance estimates • Average error after uniform calibration (linear regression) 21% • Iterative: 19.7%, Mean: 16.0% • Joint: 10.1%

  7. Auto Calibration • Use information about application to help calibration process • Localization: • dij = dji • dij + djk – dik >= 0 • Can use these to help constrain problem • -

  8. Comments • Not truly adhoc yet • Where is computation performed? • Accuracy of calibration vs. number of estimates

More Related