  Wireless sensor networks are often designed to perform two tasks: sensing a
physical field and transmitting the data to end-users. A crucial aspect of the
design of a WSN is the minimization of the overall energy consumption. Previous
researchers aim at optimizing the energy spent for the communication, while
mostly ignoring the energy cost due to sensing. Recently, it has been shown
that considering the sensing energy cost can be beneficial for further
improving the overall energy efficiency. More precisely, sparse sensing
techniques were proposed to reduce the amount of collected samples and recover
the missing data by using data statistics. While the majority of these
techniques use fixed or random sampling patterns, we propose to adaptively
learn the signal model from the measurements and use the model to schedule when
and where to sample the physical field. The proposed method requires minimal
on-board computation, no inter-node communications and still achieves appealing
reconstruction performance. With experiments on real-world datasets, we
demonstrate significant improvements over both traditional sensing schemes and
the state-of-the-art sparse sensing schemes, particularly when the measured
data is characterized by a strong intra-sensor (temporal) or inter-sensors
(spatial) correlation.
