Missing sensor data interpolation

Abstract

Ubiquitous sensor stations continuously measure several geophysical variables over large zones and long (potentially unbounded) periods of time. However, observations can cover neither every space location nor every time. Interpolation, i.e., the estimation of unknown data in each location or time of interest, can be used to supplement station records. Although in GIScience there has been a tendency to treat space and time separately, there is now great interest in analyzing data in both the domains. This suggests that integrating space and time would yield better results than treating them separately, when interpolating several geophysical fields. This chapter contributes to the investigation of spatiotemporal interpolators in a remote-sensing scenario. We describe two interpolation techniques, which use trend clusters to interpolate missing data. The former performs the estimation phase by using the Inverse Distance Weighting approach, while the latter uses Kriging. Both have been adapted to a sensor network scenario. The proposed techniques have been evaluated in a large air-climate sensor network. The empirical study compares the accuracy and efficiency of both techniques.