To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
This work addresses resource optimization for edge computing applications like smart sensing in drones and self-driving vehicles, but it is incremental as it builds on existing estimation and RL methods.
The paper tackles the latency-accuracy trade-off in edge computing networks by proposing a Reinforcement Learning-based approach to dynamically allocate computational resources at sensors, showing improved monitoring performance through online sensor selection under constrained computation.
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.