计算还是不计算？资源受限边缘计算中的自适应智能传感
To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
AI总结：
针对边缘计算中智能传感器网络的延迟-精度权衡问题，提出嵌入计算与通信延迟的估计理论优化框架及基于强化学习的动态资源分配方法，经实验验证有效，且基站计算受限下在线传感器选择可进一步提升监测性能。
摘要：
我们考虑一个用于边缘计算应用的智能传感器网络，该网络对时变信号进行采样并将更新发送到基站以进行远程全局监测。传感器配备了传感和计算功能，可以发送原始数据或在传输前在板上处理数据。边缘的有限硬件资源产生了基本的延迟-精度权衡：原始测量不准确但及时，而准确的处理后更新则在处理延迟后可用。因此，需要决定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测性能。为解决这一传感设计问题，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，并提出了一种基于强化学习的方法，动态分配每个传感器的计算资源。我们提出的方法的有效性通过以无人机互联网和自动驾驶车辆智能传感为背景的数值实验得到验证。特别是，我们表明，在基站计算受限的情况下，通过在线传感器选择可以进一步提高监测性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
