A collaborative approach to in-place sensor calibration

  • Authors:

  • Vladimir Bychkovskiy;Seapahn Megerian;Deborah Estrin;Miodrag Potkonjak

  • Affiliations:

  • Department of Computer Science, University of California, Los Angeles;Department of Computer Science, University of California, Los Angeles;Department of Computer Science, University of California, Los Angeles;Department of Computer Science, University of California, Los Angeles

  • Venue:
  • IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
  • Year:
  • 2003

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Abstract

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 difficult task since sensor nodes are often not easily accessible and manual device-by-device calibration is intractable. In this paper, we present a two-phase post-deployment calibration technique for large-scale, dense sensor deployments. In its first 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 first phase is to use temporal correlation of signals received at neighboring sensors when the signals are highly correlated (i.e. sensors are observing the same phenomenon) 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 the first phase of the algorithm using empirical and simulated data.