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<p>This paper studies the task scheduling problem in the multiple unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system. In order to ensure low delay and high energy utilization, a multi-layer heterogeneous task scheduling strategy is proposed. The strategy allows system model to simulate arrival and service of tasks by applying heterogeneous M/M/1, M/M/c, and M/M/c/K classical queues. In order to better evaluate the proposed strategy, we obtain various performance measures. By trading off delay and energy consumption, a single-objective optimization problem with constraints is formulated. Then we introduce the penalty function method to transform the problem into a single-objective optimization problem without any constraints, and propose a hierarchical heterogeneous task scheduling optimization (HHTSO) algorithm to optimize the formulated problem. Theoretical analysis and simulation results show that the proposed algorithm is more effective in ensuring low delay and low energy consumption.</p>

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A Multi-layer heterogeneous task scheduling strategy in UAVs-assisted MEC networks

  • 期刊论文

  • 发表日期：

    2025年8月19日

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  • ShanChen Pang,
  • XiaoRan Zhu,
  • Haiyuan Gui,
  • Yanxiang Zhang

摘要

This paper studies the task scheduling problem in the multiple unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system. In order to ensure low delay and high energy utilization, a multi-layer heterogeneous task scheduling strategy is proposed. The strategy allows system model to simulate arrival and service of tasks by applying heterogeneous M/M/1, M/M/c, and M/M/c/K classical queues. In order to better evaluate the proposed strategy, we obtain various performance measures. By trading off delay and energy consumption, a single-objective optimization problem with constraints is formulated. Then we introduce the penalty function method to transform the problem into a single-objective optimization problem without any constraints, and propose a hierarchical heterogeneous task scheduling optimization (HHTSO) algorithm to optimize the formulated problem. Theoretical analysis and simulation results show that the proposed algorithm is more effective in ensuring low delay and low energy consumption.