3D UAV Trajectory Planning for IoT Data Collection Over 3D Terrain Features

UAVs are increasingly essential in wireless communication applications, such as internet of things (IoT) and sensor networks, due to their agile mobility. However, planning three-dimensional (3D) UAV trajectories over a continuous temporal-spatial domain remains challenging due to the computational complexity of non-convex optimization. This paper addresses UAV-assisted IoT data collection, aiming to minimize total energy consumption while considering UAV capabilities, heterogeneous IoT data demands, and 3D terrain. We propose a matrix-based differential evolution with constraint handling (MDE-CH), a computationally efficient algorithm for solving constrained non-convex optimization problems. Numerical results show that MDE-CH efficiently generates continuous 3D UAV trajectories, significantly reducing energy consumption and outperforming the conventional fly-hover-fly model for 2D and 3D trajectory planning.