We consider a network of smart sensors for edge computing application that sample a signal of interest 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 computational delay. Also, if sensor on-board processing entails data compression, latency caused by wireless communication might be higher for raw measurements. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize overall network performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds computation and communication delays, and propose a Reinforcement Learning-based approach to dynamically allocate computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical simulations with case studies motivated by the Internet of Drones and self-driving vehicles.

翻译：我们考虑一个用于边缘计算应用的智能传感器网络，该网络对感兴趣信号进行采样，并将更新发送至基站进行远程全局监测。传感器配备感知和计算能力，可选择发送原始数据或在传输前进行板载处理。边缘端有限的硬件资源导致基本的延迟-精度权衡：原始测量值不精确但及时，而精确的处理后更新则需在计算延迟后才能获得。此外，若传感器板载处理涉及数据压缩，原始测量值因无线通信引起的延迟可能更高。因此，需要决策传感器何时传输原始测量值或依赖本地处理以最大化整体网络性能。为解决这一感知设计问题，我们建立了一个嵌入计算和通信延迟的估计理论优化框架，并提出一种基于强化学习的方法来动态分配每个传感器的计算资源。通过以无人机互联网和自动驾驶车辆为案例的数值仿真验证了所提方法的有效性。