  With increasingly more computation being shifted to the edge of the network,
monitoring of critical infrastructures, such as intermediate processing nodes
in autonomous driving, is further complicated due to the typically
resource-constrained environments. In order to reduce the resource overhead on
the network link imposed by monitoring, various methods have been discussed
that either follow a filtering approach for data-emitting devices or conduct
dynamic sampling based on employed prediction models. Still, existing methods
are mainly requiring adaptive monitoring on edge devices, which demands device
reconfigurations, utilizes additional resources, and limits the sophistication
of employed models.
  In this paper, we propose a sampling-based and cloud-located approach that
internally utilizes probabilistic forecasts and hence provides means of
quantifying model uncertainties, which can be used for contextualized
adaptations of sampling frequencies and consequently relieves constrained
network resources. We evaluate our prototype implementation for the monitoring
pipeline on a publicly available streaming dataset and demonstrate its positive
impact on resource efficiency in a method comparison.
