Distributed In-Place Calibration in Sensor Networks

of the Thesis University of California, Los Angeles, 2003 Professor Deborah Estrin, Chair Numerous factors contribute to errors in sensor measurements. In order to be useful, any sensor device must be calibrated to adjust its accuracy against the expected measurement scale. In large-scale sensor networks, calibration will be an exceptionally dicult task since sensor nodes are often not easily accessible and manual device-by-device calibration is intractable. In this thesis, we present a two-phase post-deployment calibration technique for large-scale, dense sensor deployments. In its rst phase, the algorithm derives relative calibration relationships between pairs of co-located sensors, while in the second phase, it maximizes the consistency of the pair-wise calibration functions among groups of sensor nodes. The key idea in the rst phase is to use temporal correlation of signals received at neighboring sensors to derive the function relating their bias in amplitude. We formulate the second phase as an optimization problem and present an algorithm suitable for localized implementation. We evaluate the performance of both phases of the algorithm using detailed simulation