# Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2309.14757 · HTML: https://mlsanity.com/paper/2309.14757/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks](https://mlsanity.com/paper/1905.02993/) (2019)
- [Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks](https://mlsanity.com/paper/2003.00391/) (2020)
- [Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge](https://mlsanity.com/paper/2301.03423/) (2023)
- [A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks](https://mlsanity.com/paper/2209.09206/) (2022)
- [3-D Deployment of UAV Swarm for Massive MIMO Communications](https://mlsanity.com/paper/2105.00950/) (2021)

## Built on by (most cited first, 5 of 5)

- [Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.08421/) (2024)
- [An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management](https://mlsanity.com/paper/2501.12991/) (2025)
- [An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications](https://mlsanity.com/paper/2410.07208/) (2024)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)
- [AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization](https://mlsanity.com/paper/2601.08491/) (2026)

## Abstract (arXiv)

In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.