Edge Computing Based Multi-Objective Task Scheduling Strategy for UAV with Limited Airborne Resources

The unmanned aerial vehicles often suffer from insufficient computing power due to the limited onboard resources, resulting in task delays under heavy tasks. A system based on edge computing was constructed to solve this problem, which involved task allocation center, unmanned aerial vehicle group, data node, and power supply station. A mathematical optimization framework based on task, resource, and scheduling models was proposed, and the non-dominated sorting genetic algorithm III was used. The objective optimization was efficiently processed through genetic operations, non-dominated sorting, and reference point-based selection mechanisms. These results confirmed that the non-dominated sorting genetic algorithm III performed well in comprehensive performance evaluation, with an MS index of 0.881 in large-scale map tests and an AQ index of 0.133 in medium-sized maps. The calculation time was 58.9 seconds, 140.5 seconds, and 545.3 seconds in small, medium, and large map tests, respectively, leading other algorithms. Therefore, the designed model has excellent performance in task quality, time extension, and computational efficiency, which has application value. © 2024 Slovene Society Informatika. All rights reserved.