# The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments

arXiv: https://arxiv.org/abs/2403.07923 · HTML: https://mlsanity.com/paper/2403.07923/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV](https://mlsanity.com/paper/2009.01502/) (2020)
- [Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles](https://mlsanity.com/paper/2201.11295/) (2022)

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

- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)

## Abstract (arXiv)

In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control.