An on-demand compressed sensing approach for spatial monitoring of correlated big data using multi-contours in dense wireless sensor network

A novel on-demand, adaptive compressed sensing approach is proposed and discussed for spatial monitoring of correlated massive data in dense wireless sensor field. The spatial monitoring is based on collecting the sensor observations of a subset of sensors that their reading is in Δ margin of a set of contour levels. The contour level set is adaptively updated in order to reduce the mean square error of reconstruction of the spatial distribution. The reconstruction error and the number of involved sensing nodes as cost are used to compare the proposed approach with spatial monitoring using equally spaced contour levels.