# To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

arXiv: https://arxiv.org/abs/2209.02166 · HTML: https://mlsanity.com/paper/2209.02166/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 114 references that are in the map.
- First posted on arXiv 5 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [Is Q-learning Provably Efficient?](https://mlsanity.com/paper/1807.03765/) (2018)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review](https://mlsanity.com/paper/2005.09830/) (2020)
- [Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://mlsanity.com/paper/2001.10402/) (2020)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Decentralized Federated Learning via SGD over Wireless D2D Networks](https://mlsanity.com/paper/2002.12507/) (2020)
- [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)
- [Wireless Network Design for Control Systems: A Survey](https://mlsanity.com/paper/1708.07353/) (2017)
- [Multi-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraints](https://mlsanity.com/paper/2005.03650/) (2020)
- [Data-Driven Control of Complex Networks](https://mlsanity.com/paper/2003.12189/) (2020)
- [Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis](https://mlsanity.com/paper/2011.10758/) (2020)
- [Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals](https://mlsanity.com/paper/1905.07020/) (2019)
- [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)
- [The Cost of Delay in Status Updates and their Value: Non-linear Ageing](https://mlsanity.com/paper/1812.09320/) (2018)
- [Age Optimal Information Gathering and Dissemination on Graphs](https://mlsanity.com/paper/1901.02178/) (2019)
- [A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing](https://mlsanity.com/paper/1901.07216/) (2019)
- [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)
- [A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems](https://mlsanity.com/paper/2204.00703/) (2022)
- [Leveraging Linear Quadratic Regulator Cost and Energy Consumption for Ultra-Reliable and Low-Latency IoT Control Systems](https://mlsanity.com/paper/2002.07354/) (2020)
- [Sensor Scheduling for Linear Systems: A Covariance Tracking Approach](https://mlsanity.com/paper/2110.08924/) (2021)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs](https://mlsanity.com/paper/2505.06319/) (2025)

## Abstract (arXiv)

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.