  Data stream processing is an increasingly important topic due to the
prevalence of smart devices and the demand for real-time analytics.
Geo-distributed streaming systems, where cloud-based queries utilize data
streams from multiple distributed devices, face challenges since wide-area
network (WAN) bandwidth is often scarce or expensive. Edge computing allows us
to address these bandwidth costs by utilizing resources close to the devices,
e.g. to perform sampling over the incoming data streams, which trades
downstream query accuracy to reduce the overall transmission cost. In this
paper, we leverage the fact that correlations between data streams may exist
across devices located in the same geographical region. Using this insight, we
develop a hybrid edge-cloud system which systematically trades off between
sampling at the edge and estimation of missing values in the cloud to reduce
traffic over the WAN. We present an optimization framework which computes
sample sizes at the edge and systematically bounds the number of samples we can
estimate in the cloud given the strength of the correlation between streams.
Our evaluation with three real-world datasets shows that compared to existing
sampling techniques, our system could provide comparable error rates over
multiple aggregate queries while reducing WAN traffic by 27-42%.
