PROJECT TITLE :
DASS: Distributed Adaptive Sparse Sensing
Wireless sensor networks are usually designed to perform two tasks: sensing a physical field and transmitting the data to end-users. A crucial design facet of a WSN is the minimization of the general energy consumption. Previous researchers aim at optimizing the energy spent for the communication, whereas largely ignoring the energy cost of sensing. Recently, it has been shown that considering the sensing energy cost can be useful for any improving the overall energy efficiency. A lot of precisely, sparse sensing techniques were proposed to scale back the number of collected samples and recover the missing data using knowledge statistics. Whereas the majority of these techniques use fixed or random sampling patterns, we tend to propose adaptively learning the signal model from the measurements and using the model to schedule when and where to sample the physical field. The proposed method needs minimal on-board computation, no inter-node communications, and achieves appealing reconstruction performance. With experiments on real-world datasets, we have a tendency to demonstrate important enhancements over both ancient sensing schemes and the state-of-the-art sparse sensing schemes, significantly when the measured data is characterized by a sturdy intra-sensor (temporal) or inter-sensors (spatial) correlation.
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