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Missing Temperature Data Recovery Methods Based on Smoothness, Bandlimitedness and Sparseness Priors

In this paper, three missing temperature data recovery methods using smoothness, bandlimitedness and sparseness priors are presented. First, the temperature data collected from the sensor network is represented by graph signal such that graph Laplacian matrix (GLM) and graph Fourier transform (GFT) can be employed to develop the missing data recovery methods. Second, the smoothness measure of graph signal is defined by GLM and the recovery problem based on smoothness prior is formulated as an optimization problem whose solution can be obtained by solving the matrix inversion. Third, a recovery method based on bandlimitedness prior in GFT domain is studied and an iterative method is used to get the recovery data. Fourth, the sparseness prior in GFT domain is applied to estimate the missing temperature data by the iterative thresholding method. Finally, real temperature data collected in Taiwan is used to evaluate the performance of three recovery methods based on different priors.