  Although wireless sensor networks (WSNs) are powerful in monitoring physical
events, the data collected from a WSN are almost always incomplete if the
surveyed physical event spreads over a wide area. The reason for this
incompleteness is twofold: i) insufficient network coverage and ii) data
aggregation for energy saving. Whereas the existing recovery schemes only
tackle the second aspect, we develop Dual-lEvel Compressed Aggregation (DECA)
as a novel framework to address both aspects. Specifically, DECA allows a high
fidelity recovery of a widespread event, under the situations that the WSN only
sparsely covers the event area and that an in-network data aggregation is
applied for traffic reduction. Exploiting both the low-rank nature of
real-world events and the redundancy in sensory data, DECA combines matrix
completion with a fine-tuned compressed sensing technique to conduct a
dual-level reconstruction process. We demonstrate that DECA can recover a
widespread event with less than 5% of the data, with respect to the dimension
of the event, being collected. Performance evaluation based on both synthetic
and real data sets confirms the recovery fidelity and energy efficiency of our
DECA framework.
