论文标题
要计算还是不计算？自适应智能感测在资源受限的边缘计算中
To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
论文作者
论文摘要
我们考虑了一个智能传感器网络，用于对边缘计算应用程序进行采样，该应用程序采样了一个随时间变化的信号，并将更新发送到基站以进行远程全局监视。传感器配备了传感和计算，并且可以在传输前在板上发送原始数据或处理它们。边缘有限的硬件资源产生了基本的潜伏 - 准确性权衡：原始测量值不准确，但及时，而在处理延迟后，准确的处理更新可用。因此，需要决定何时传感器应传输原始测量或依靠本地处理以最大程度地提高网络监视性能。为了解决这个传感设计问题，我们对估计理论优化框架进行建模，该框架嵌入了计算和通信延迟，并提出了一种基于增强学习的方法，该方法可以动态地分配每个传感器的计算资源。我们提出的方法的有效性是通过针对无人机和自动驾驶汽车的智能感测的数值实验来验证的。特别是，我们表明，在基站的约束计算下，可以通过在线传感器选择进一步提高监视性能。
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.
