Path Planning for Large-Scale Data Collection in UAV-Assisted loT System

Unmanned aerial vehicle (UAV) is deemed the most prospective technique for data collection in the future Internet of Things (IoT), due to its flexible deployment, potential mobility, and low cost. In order to ensure that UAV s can choose the most efficient and reliable flight route in complex environments, we propose a novel path planning scheme for data collection in a large-scale loT system based on Hilbert curve and ge-netic algorithm (HC-GA). We aim to minimize the total path length of the UAV by optimizing the flight path of the UA V. Meanwhile, the simulation time of the proposed algorithm is optimized to ensure real-time. Simulation results demonstrate that the proposed scheme could reduce the path length efficiently compared with benchmark algorithms. The simulation time cost for path planning is limited to a satisfactory volume, especially for scenarios with a large scale of sensor nodes.