# UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer

arXiv: https://arxiv.org/abs/2401.02425 · HTML: https://mlsanity.com/paper/2401.02425/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Learning Improvement Heuristics for Solving Routing Problems](https://mlsanity.com/paper/1912.05784/) (2019)
- [The Transformer Network for the Traveling Salesman Problem](https://mlsanity.com/paper/2103.03012/) (2021)
- [Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications](https://mlsanity.com/paper/2012.01489/) (2020)
- [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)
- [UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning](https://mlsanity.com/paper/2108.00354/) (2021)
- [Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2003.04816/) (2020)
- [Approximation algorithms for TSP with neighborhoods in the plane](https://mlsanity.com/paper/1703.01640/) (2017)
- [Average Peak Age-of-Information Minimization in UAV-assisted IoT Networks](https://mlsanity.com/paper/1804.06543/) (2018)
- [Energy Management and Trajectory Optimization for UAV-Enabled Legitimate Monitoring Systems](https://mlsanity.com/paper/2004.10918/) (2020)
- [Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization](https://mlsanity.com/paper/2006.13610/) (2020)
- [Joint Optimization of Trajectory, Propulsion and Thrust Powers for Covert UAV-on-UAV Video Tracking and Surveillance](https://mlsanity.com/paper/2012.11887/) (2020)

## Built on by (most cited first, 6 of 6)

- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges](https://mlsanity.com/paper/2508.09561/) (2025)
- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)
- [Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT](https://mlsanity.com/paper/2503.06145/) (2025)
- [Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems](https://mlsanity.com/paper/2502.17517/) (2025)
- [Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks](https://mlsanity.com/paper/2606.16331/) (2026)

## Abstract (arXiv)

Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. By taking into account age-of-information (AoI), we investigate the trajectory planning problem of an unmanned aerial vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network. Since the total AoI of the IoT network depends on the flight time of the UAV and the data collection time at hovering points, we jointly optimize the selection of hovering points and the visiting order to these points. We exploit the state-of-the-art transformer and the weighted A*, which is a path search algorithm, to design a machine learning algorithm to solve the formulated problem. The whole UAV-IoT system is fed into the encoder network of the proposed algorithm, and the algorithm's decoder network outputs the visiting order to ground clusters. Then, the weighted A* is used to find the hovering point for each cluster in the ground IoT network. Simulation results show that the trained model by the proposed algorithm has a good generalization ability to generate solutions for IoT networks with different numbers of ground clusters, without the need to retrain the model. Furthermore, results show that our proposed algorithm can find better UAV trajectories with the minimum total AoI when compared to other algorithms.