  A significantly low cost and tractable progressive learning approach is
proposed and discussed for efficient spatiotemporal monitoring of a completely
unknown, two dimensional correlated signal distribution in localized wireless
sensor field. The spatial distribution is compressed into a number of its
contour lines and only those sensors that their sensor observations are in a
$\Delta$ margin of the contour levels are reporting to the information fusion
center (IFC). The proposed algorithm progressively finds the model parameters
in iterations, by using extrapolation in curve fitting, and stochastic gradient
method for spatial monitoring. The IFC tracks the signal variations using these
parameters, over time. The monitoring performance and the cost of the proposed
algorithm are discussed, in this letter.
