  In this paper, we consider a wireless network of smart sensors (agents) that
monitor a dynamical process and send measurements to a base station that
performs global monitoring and decision-making. Smart sensors are equipped with
both sensing and computation, and can either send raw measurements or process
them prior to transmission. Constrained agent resources raise a fundamental
latency-accuracy trade-off. On the one hand, raw measurements are inaccurate
but fast to produce. On the other hand, data processing on resource-constrained
platforms generates accurate measurements at the cost of non-negligible
computation latency. Further, if processed data are also compressed, latency
caused by wireless communication might be higher for raw measurements. Hence,
it is challenging to decide when and where sensors in the network should
transmit raw measurements or leverage time-consuming local processing. To
tackle this design problem, we propose a Reinforcement Learning approach to
learn an efficient policy that dynamically decides when measurements are to be
processed at each sensor. Effectiveness of our proposed approach is validated
through a numerical simulation with case study on smart sensing motivated by
the Internet of Drones.
