  In IoT solutions, it is usually desirable to collect data from a large number
of distributed IoT sensors at a central node in the cloud for further
processing. One of the main design challenges of such solutions is the high
communication overhead between the sensors and the central node (especially for
multimedia data). In this paper, we aim to reduce the communication overhead
and propose a method that is able to determine which sensors should send their
data to the central node and which to drop data. The idea is that some sensors
may have data which are correlated with others and some may have data that are
not essential for the operation to be performed at the central node. As such
decisions are application dependent and may change over time, they should be
learned during the operation of the system, for that we propose a method based
on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns
which sensor's data is cost-effective to be sent to the central node. The
proposed approach has been evaluated on a multi-view multi-camera dataset, and
we observe a significant reduction in communication overhead with marginal
degradation in object classification accuracy.

Networking and Internet Architecture;Machine Learning;Signal Processing