A method for simultaneously implementing trajectory planning and DAG task scheduling in multi-UAV assisted edge computing

Abstract

UAV-assisted edge computing(UEC) as a new framework is able to provide computing services to remote areas. However, facing computationally intensive tasks with huge computation time forces them to hover near the user’s devices(UDs) for long periods of time. To better utilize the available arithmetic resources and reduce the computation time of UAVs, it is imperative to introduce directed acyclic graph (DAG) task scheduling into the UEC framework. Therefore, this article proposes a DAG-type task-driven trajectory planning (DAG-TDTP) model, which can plan UAV routes while scheduling DAG subtasks between UAVs that offload from UDs. To implement the DAG-TDTP model, we propose a distance-based heterogeneous earliest-finish-time (D-HEFT) algorithm and a time segmentation method based on the cooperative task offloading matrix. To stimulate the potential of the DAG-TDTP model in reducing energy consumption, we propose a genetic algorithm based on temporary key nodes (TKNGA) for the proposed model. Through simulation analysis, we verify the superiority of the proposed model in reducing UAV system energy consumption and the superiority of TKNGA compared to other algorithms.