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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Energy-Efficient UAV-Enabled MEC System: Bits Allocation Optimization and Trajectory Design
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Energy-Efficient UAV-Enabled MEC System: Bits Allocation Optimization and Trajectory Design

机译：节能的启用了无人机的MEC系统：位分配优化和轨迹设计

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摘要

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings.
机译：由于具有高移动性和易于部署的优势，无人飞行器（UAV）支持的移动边缘计算（MEC）系统因其低延迟和低传输能耗的潜力而吸引了众多关注。它已被广泛应用于提供通信和计算服务，尤其是在物联网（IoT）中。但是，在支持无人机的MEC系统中仍然存在一些挑战。首先，无人机的耐久性受到限制，并进一步影响系统的性能。其次，移动设备由电池供电，并且某些设备的电池难以更换。因此，本文研究了一种具有无人机功能的MEC系统，在该系统中，无人机具有计算能力并提供任务卸载服务。在无人机的能源预算，每个任务的位数，因果关系的约束下，使启用无人机的系统的总能耗（包括无人机的能耗和地面用户的能耗）最小化。数据和无人机的速度。为了最小化总能耗，联合优化了上传数据，计算数据，下载数据的位分配和无人机的轨迹。此外，提出了一种两阶段交替算法来解决非凸公式化问题。最后，仿真结果表明了该方案与其他基准方案相比的优越性。最后，在不同的设置下证明了该方案的性能。

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