# Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks

arXiv: https://arxiv.org/abs/2003.00391 · HTML: https://mlsanity.com/paper/2003.00391/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2107.11015/) (2021)
- [UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer](https://mlsanity.com/paper/2401.02425/) (2023)
- [Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach](https://mlsanity.com/paper/2309.14757/) (2023)
- [A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks](https://mlsanity.com/paper/2209.09206/) (2022)
- [Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks](https://mlsanity.com/paper/2306.02029/) (2023)
- [Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models](https://mlsanity.com/paper/2401.13827/) (2024)
- [AoI-minimizing Scheduling in UAV-relayed IoT Networks](https://mlsanity.com/paper/2107.05181/) (2021)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)
- [Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2601.14092/) (2026)

## Abstract (arXiv)

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.