DECA: Recovering Fields of Physical Quantities from Incomplete Sensory Data
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: insufficient network coverage and data aggregation for energy saving. Whereas the existing recovery schemes only tackle the second aspect, the authors 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.