  This paper advocates the use of the distributed compressed sensing (DCS)
paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for
energy self-sustainability. We consider networks with signal/energy models that
capture the fact that both the collected signals and the harvested energy of
different devices can exhibit correlation. We provide theoretical analysis on
the performance of both the classical compressive sensing (CS) approach and the
proposed distributed CS (DCS)-based approach to data acquisition for EH IoT.
Moreover, we perform an in-depth comparison of the proposed DCS-based approach
against the distributed source coding (DSC) system. These performance
characterizations and comparisons embody the effect of various system phenomena
and parameters including signal correlation, EH correlation, network size, and
energy availability level. Our results unveil that, the proposed approach
offers significant increase in data gathering capability with respect to the
CS-based approach, and offers a substantial reduction of the mean-squared error
distortion with respect to the DSC system.
