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%

8. Auto Calibration Use information about application to help calibration process Localization: –d ij = d ji –d ij + dj k – d ik >= 0 –Can use these to help constrain problem -

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