  Compressive Sensing (CS) method is a burgeoning technique being applied to
diverse areas including wireless sensor networks (WSNs). In WSNs, it has been
studied in the context of data gathering and aggregation, particularly aimed at
reducing data transmission cost and improving power efficiency. Existing CS
based data gathering work in WSNs assume fixed and uniform compression
threshold across the network, regard- less of the data field characteristics.
In this paper, we present a novel data aggregation architecture model that
combines a multi- resolution structure with compressed sensing. The compression
thresholds vary over the aggregation hierarchy, reflecting the underlying data
field. Compared with previous relevant work, the proposed model shows its
significant energy saving from theoretical analysis. We have also implemented
the proposed CS- based data aggregation framework on a SIDnet SWANS platform,
discrete event simulator commonly used for WSN simulations. Our experiments
show substantial energy savings, ranging from 37% to 77% for different nodes in
the networking depending on the position of hierarchy.
