  We consider a centralized detection problem where sensors experience noisy
measurements and intermittent connectivity to a centralized fusion center. The
sensors collaborate locally within predefined sensor clusters and fuse their
noisy sensor data to reach a common local estimate of the detected event in
each cluster. The connectivity of each sensor cluster is intermittent and
depends on the available communication opportunities of the sensors to the
fusion center. Upon receiving the estimates from all the connected sensor
clusters the fusion center fuses the received estimates to make a final
determination regarding the occurrence of the event across the deployment area.
We refer to this hybrid communication scheme as a \emph{cloud-cluster}
architecture. We propose a method for optimizing the decision rule for each
cluster and analyzing the expected detection performance resulting from our
hybrid scheme. Our method is tractable and addresses the high computational
complexity caused by heterogeneous sensors' and clusters' detection quality,
heterogeneity in their communication opportunities, and non-convexity of the
loss function. Our analysis shows that clustering the sensors provides
resilience to noise in the case of low sensor communication probability with
the cloud. For larger clusters, a steep improvement in detection performance is
possible even for a low communication probability by using our cloud-cluster
architecture.
