3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation
<|reference_start|>3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation: 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.<|reference_end|>
@article{sun20243d,
    title={3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based
  Evolutionary Computation},
    author={Pei-Fa Sun, Yujae Song, Kang-Yu Gao, Yu-Kai Wang, Changjun Zhou,
  Sang-Woon Jeon, Jun Zhang},
    journal={arXiv preprint arXiv:2410.05759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05759},
    primaryClass={cs.NE}
}