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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.05759 |
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| _version_ | 1866910640944185344 |
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| author | Sun, Pei-Fa Song, Yujae Gao, Kang-Yu Wang, Yu-Kai Zhou, Changjun Jeon, Sang-Woon Zhang, Jun |
| author_facet | Sun, Pei-Fa Song, Yujae Gao, Kang-Yu Wang, Yu-Kai Zhou, Changjun Jeon, Sang-Woon Zhang, Jun |
| contents | UAVs are increasingly becoming vital tools in various wireless communication applications including internet of things (IoT) and sensor networks, thanks to their rapid and agile non-terrestrial mobility. Despite recent research, planning three-dimensional (3D) UAV trajectories over a continuous temporal-spatial domain remains challenging due to the need to solve computationally intensive optimization problems. In this paper, we study UAV-assisted IoT data collection aimed at minimizing total energy consumption while accounting for the UAV's physical capabilities, the heterogeneous data demands of IoT nodes, and 3D terrain. We propose a matrix-based differential evolution with constraint handling (MDE-CH), a computation-efficient evolutionary algorithm designed to address non-convex constrained optimization problems with several different types of constraints. Numerical evaluations demonstrate that the proposed MDE-CH algorithm provides a continuous 3D temporal-spatial UAV trajectory capable of efficiently minimizing energy consumption under various practical constraints and outperforms the conventional fly-hover-fly model for both two-dimensional (2D) and 3D trajectory planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation Sun, Pei-Fa Song, Yujae Gao, Kang-Yu Wang, Yu-Kai Zhou, Changjun Jeon, Sang-Woon Zhang, Jun Neural and Evolutionary Computing UAVs are increasingly becoming vital tools in various wireless communication applications including internet of things (IoT) and sensor networks, thanks to their rapid and agile non-terrestrial mobility. Despite recent research, planning three-dimensional (3D) UAV trajectories over a continuous temporal-spatial domain remains challenging due to the need to solve computationally intensive optimization problems. In this paper, we study UAV-assisted IoT data collection aimed at minimizing total energy consumption while accounting for the UAV's physical capabilities, the heterogeneous data demands of IoT nodes, and 3D terrain. We propose a matrix-based differential evolution with constraint handling (MDE-CH), a computation-efficient evolutionary algorithm designed to address non-convex constrained optimization problems with several different types of constraints. Numerical evaluations demonstrate that the proposed MDE-CH algorithm provides a continuous 3D temporal-spatial UAV trajectory capable of efficiently minimizing energy consumption under various practical constraints and outperforms the conventional fly-hover-fly model for both two-dimensional (2D) and 3D trajectory planning. |
| title | 3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2410.05759 |