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.

翻译：我们针对边缘计算应用中的智能传感器网络展开研究，该网络对时变信号进行采样并向基站发送更新以实现远程全局监控。传感器配备感知与计算能力，既可传输原始数据，也可在传输前进行机载处理。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实时性高，而精确的处理后更新则需在计算延迟后方可获取。因此，需要决策传感器何时应传输原始测量值或依赖本地处理以最大化网络监控性能。为应对这一感知设计问题，我们构建了嵌入了计算与通信延迟的估计理论优化框架，并提出基于强化学习的方法来动态分配各传感器的计算资源。通过面向无人机互联网与自动驾驶车辆的智能感知数值实验，验证了所提方法的有效性。特别地，我们证明在基站计算资源受限条件下，通过在线传感器选择可进一步提升监控性能。