Near-optimal collecting data strategy based on ordinary Kiriging variance

When monitoring spatial phenomena, we are not just interested in measurements at sensed locations but also at locations where no sensors were placed. To estimate the scalar field where no sensors are deployed, we need to interpolate the data. We are interested in how the best sampling design is to be found and best used to draw conclusions about the field as whole. First of all, a performance metric is defined to quantify how well the sampling network collecting data in a given region. Secondly, near-optimal collecting data strategy proposed minimizes the integral of the Kriging variance over the area of interest. Thirdly, several approaches proposed make the optimization more computationally efficient. Finally, the proposed methods are verified respectively by simulation.