Abstract
In this paper, an online infield nonparametric calibration and error-modeling approach was developed. The approach employs a single source as the external stimulus that creates the differential sensor readings used for calibration. Under very mild assumptions imposed on the calibration functions, error model, and the environment, the technique utilizes the maximal likelihood principle and a nonlinear function minimization solver to derive both the calibration function and the error model of a specified accuracy simultaneously. The approach is intrinsically localized and presents two variants: 1) one where only pairs of neighboring sensors have to communicate in order to conduct the calibration and 2) one where probably a minimum amount of communication is achieved. In addition, the broadcasting tree problem was also formulated as an integer linear programming (ILP) instance; therefore, the broadcasts used in the second variant are optimally resolved. The techniques were evaluated using the traces from the light sensors recorded by the infield deployed sensors, and the statistical evaluations are conducted in order to obtain the interval of confidence to support all the results
Published Version
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