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.

通过支持感知和计算，智能传感器在边缘计算应用中采样并发送感兴趣的信号更新，从而形成一个网络，可远程全局监控传感器的性能。为了解决原始测量的不准确和及时性问题，提出了一个强化学习算法来动态分配计算资源。数值仿真结果表示，提议的方法在无人机网络和自动驾驶汽车等应用场景中比传统方法更有效。

如何计算？——资源受限的边缘计算中的自适应智能感知