Macro-Calibration in Sensor/Actuator Networks

# Macro-Calibration in Sensor/Actuator Networks

## Macro-Calibration in Sensor/Actuator Networks

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##### 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