  We consider a network of smart sensors for an edge computing application that
sample a time-varying signal and send updates to a base station for remote
global monitoring. Sensors are equipped with sensing and compute, and can
either send raw data or process them on-board before transmission. Limited
hardware resources at the edge generate a fundamental latency-accuracy
trade-off: raw measurements are inaccurate but timely, whereas accurate
processed updates are available after processing delay. Hence, one needs to
decide when sensors should transmit raw measurements or rely on local
processing to maximize network monitoring performance. To tackle this sensing
design problem, we model an estimation-theoretic optimization framework that
embeds both computation and communication latency, and propose a Reinforcement
Learning-based approach that dynamically allocates computational resources at
each sensor. Effectiveness of our proposed approach is validated through
numerical experiments motivated by smart sensing for the Internet of Drones and
self-driving vehicles. In particular, we show that, under constrained
computation at the base station, monitoring performance can be further improved
by an online sensor selection.
