# UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.00354 · HTML: https://mlsanity.com/paper/2108.00354/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Seq2Slate: Re-ranking and Slate Optimization with RNNs](https://mlsanity.com/paper/1810.02019/) (2018)
- [Trajectory Design for Energy Minimization in UAV-enabled Wireless Communications with Latency Constraints](https://mlsanity.com/paper/1910.08612/) (2019)
- [Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning](https://mlsanity.com/paper/1907.08686/) (2019)

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

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer](https://mlsanity.com/paper/2401.02425/) (2023)
- [Non-iterative Optimization of Trajectory and Radio Resource for Aerial Network](https://mlsanity.com/paper/2405.01314/) (2024)
- [Coordinating CAV Swarms at Intersections with a Deep Learning Model](https://mlsanity.com/paper/2211.05297/) (2022)
- [Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning](https://mlsanity.com/paper/2304.10744/) (2023)
- [Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model](https://mlsanity.com/paper/2112.00333/) (2021)
- [5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul](https://mlsanity.com/paper/2202.02006/) (2022)
- [Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks](https://mlsanity.com/paper/2202.06390/) (2022)
- [Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks](https://mlsanity.com/paper/2606.16331/) (2026)
- [Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN](https://mlsanity.com/paper/2606.24483/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) have emerged as a promising candidate solution for data collection of large-scale wireless sensor networks (WSNs). In this paper, we investigate a UAV-aided WSN, where cluster heads (CHs) receive data from their member nodes, and a UAV is dispatched to collect data from CHs along the planned trajectory. We aim to minimize the total energy consumption of the UAV-WSN system in a complete round of data collection. Toward this end, we formulate the energy consumption minimization problem as a constrained combinatorial optimization problem by jointly selecting CHs from nodes within clusters and planning the UAV's visiting order to the selected CHs. The formulated energy consumption minimization problem is NP-hard, and hence, hard to solve optimally. In order to tackle this challenge, we propose a novel deep reinforcement learning (DRL) technique, pointer network-A* (Ptr-A*), which can efficiently learn from experiences the UAV trajectory policy for minimizing the energy consumption. The UAV's start point and the WSN with a set of pre-determined clusters are fed into the Ptr-A*, and the Ptr-A* outputs a group of CHs and the visiting order to these CHs, i.e., the UAV's trajectory. The parameters of the Ptr-A* are trained on small-scale clusters problem instances for faster training by using the actor-critic algorithm in an unsupervised manner. At inference, three search strategies are also proposed to improve the quality of solutions. Simulation results show that the trained models based on 20-clusters and 40-clusters have a good generalization ability to solve the UAV's trajectory planning problem in WSNs with different numbers of clusters, without the need to retrain the models. Furthermore, the results show that our proposed DRL algorithm outperforms two baseline techniques.