A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

TL;DR

This paper introduces a reinforcement learning method to optimize sensing and data transmission strategies in resource-limited wireless sensor networks, balancing latency and accuracy for improved system performance.

Contribution

It presents a novel RL-based framework for dynamic decision-making in sensor data processing and transmission in resource-constrained wireless networks.

Findings

RL approach effectively balances latency and accuracy.

Dynamic sensing policies outperform static strategies.

Simulation results demonstrate improved monitoring efficiency.

Abstract

In this paper, we consider a wireless network of smart sensors (agents) that monitor a dynamical process and send measurements to a base station that performs global monitoring and decision-making. Smart sensors are equipped with both sensing and computation, and can either send raw measurements or process them prior to transmission. Constrained agent resources raise a fundamental latency-accuracy trade-off. On the one hand, raw measurements are inaccurate but fast to produce. On the other hand, data processing on resource-constrained platforms generates accurate measurements at the cost of non-negligible computation latency. Further, if processed data are also compressed, latency caused by wireless communication might be higher for raw measurements. Hence, it is challenging to decide when and where sensors in the network should transmit raw measurements or leverage time-consuming local processing. To tackle this design problem, we propose a Reinforcement Learning approach to learn an efficient policy that dynamically decides when measurements are to be processed at each sensor. Effectiveness of our proposed approach is validated through a numerical simulation with case study on smart sensing motivated by the Internet of Drones.