  Compressive sensing (CS) is a mathematically elegant tool for reducing the
sampling rate, potentially bringing context-awareness to a wider range of
devices. Nevertheless, practical issues with the sampling and reconstruction
algorithms prevent further proliferation of CS in real world domains,
especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally
complements CS for adapting the sampling matrix, reconstructing the signal, and
learning form the compressed samples. While the CS-DL integration has received
substantial research interest recently, it has not yet been thoroughly
surveyed, nor has the light been shed on practical issues towards bringing the
CS-DL to real world implementations in the ubicomp domain. In this paper we
identify main possible ways in which CS and DL can interplay, extract key ideas
for making CS-DL efficient, identify major trends in CS-DL research space, and
derive guidelines for future evolution of CS-DL within the ubicomp domain.
