  It is of paramount importance to achieve efficient data collection in the
Internet of Things (IoT). Due to the inherent structural properties (e.g.,
sparsity) existing in many signals of interest, compressive sensing (CS)
technology has been extensively used for data collection in IoT to improve both
accuracy and energy efficiency. Apart from the existing works which leverage CS
as a channel coding scheme to deal with data loss during transmission, some
recent results have started to employ CS as a source coding strategy. The
frequently used projection matrices in these CS-based source coding schemes
include dense random matrices (e.g., Gaussian matrices or Bernoulli matrices)
and structured matrices (e.g., Toeplitz matrices). However, these matrices are
either difficult to be implemented on resource-constrained IoT sensor nodes or
have limited applicability. To address these issues, in this paper, we design a
novel simple and efficient projection matrix, named sparse Gaussian matrix,
which is easy and resource-saving to be implemented in practical IoT
applications. We conduct both theoretical analysis and experimental evaluation
of the designed sparse Gaussian matrix. The results demonstrate that employing
the designed projection matrix to perform CS-based source coding could
significantly save time and memory cost while ensuring satisfactory signal
recovery performance.
