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

arXiv: https://arxiv.org/abs/2204.00703 · HTML: https://mlsanity.com/paper/2204.00703/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping](https://mlsanity.com/paper/2011.04087/) (2020)
- [Network Offloading Policies for Cloud Robotics: a Learning-based Approach](https://mlsanity.com/paper/1902.05703/) (2019)
- [Multi-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraints](https://mlsanity.com/paper/2005.03650/) (2020)
- [Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis](https://mlsanity.com/paper/2011.10758/) (2020)
- [Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements and Challenges](https://mlsanity.com/paper/2007.12874/) (2020)
- [FPGA-based ORB Feature Extraction for Real-Time Visual SLAM](https://mlsanity.com/paper/1710.07312/) (2017)
- [LQG Control and Sensing Co-Design](https://mlsanity.com/paper/1802.08376/) (2018)
- [Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks](https://mlsanity.com/paper/1911.05859/) (2019)
- [Computation and Communication Co-Design for Real-Time Monitoring and Control in Multi-Agent Systems](https://mlsanity.com/paper/2108.03122/) (2021)

## Built on by (most cited first, 2 of 2)

- [Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs](https://mlsanity.com/paper/2407.06227/) (2024)
- [To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing](https://mlsanity.com/paper/2209.02166/) (2022)

## Abstract (arXiv)

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.