Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 71, Issue 12, Pages 13332-13343Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3200458
Keywords
Unmanned aerial vehicle (UAV); online trajectory planning; deep reinforcement learning (DRL)
Categories
Funding
- Sichuan Science and Technology Program [2021YFG0127]
Ask authors/readers for more resources
This article proposes a two-level deep reinforcement learning framework to solve the trajectory planning problem in UAV-enabled wireless sensor networks.
Due to the maneuverability and flexibility of unmanned aerial vehicles (UAVs), the UAV-enabled data collection systems for wireless sensor networks (WSN) have received widespread attention. However, the state-of-the-art UAV trajectory designs mainly focus on static environments, which are not applicable in the practical scenarios considered in this work, e.g., mobile nodes, decommissioning of existing nodes, and new emergency nodes. This article proposes a two-level deep reinforcement learning (DRL) framework to solve this challenge. The first-level deep neural network (DNN) is applied to model the dynamic changing environment. In the second level, we employ a deep Q-learning network to plan a trajectory online according to the environment features from the first level DNN. Besides, online trajectory planning is performed by a low-power UAV edge computing platform. To enable online planning on the power-constraint UAV edge-computing platform, all networks adopt a lightweight low-complexity optimization design. According to simulation results, the proposed system achieves higher data acquisition success rates when compared to existing state-of-the-art methods. We also perform field tests on the proposed UAV edge computing platform, which also demonstrates high data acquisition performance.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available