  In a resource-constrained Wireless Sensor Networks (WSNs), the optimization
of the sampling and the transmission rates of each individual node is a crucial
issue. A high volume of redundant data transmitted through the network will
result in collisions, data loss, and energy dissipation. This paper proposes a
novel data reduction scheme, that exploits the spatial-temporal correlation
among sensor data in order to determine the optimal sampling strategy for the
deployed sensor nodes. This strategy reduces the overall sampling/transmission
rates while preserving the quality of the data. Moreover, a back-end
reconstruction algorithm is deployed on the workstation (Sink). This algorithm
can reproduce the data that have not been sampled by finding the spatial and
temporal correlation among the reported data set, and filling the 'non-sampled'
parts with predictions. We have used real sensor data of a network that was
deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We
tested our approach using the previously mentioned data-set and compared it to
a recent adaptive sampling based data reduction approach. The obtained results
show that our proposed method consumes up to 60% less energy and can handle
non-stationary data more effectively.
