无人机辅助的边缘计算系统：节能资源管理框架

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"无人机辅助的多接入边缘计算系统——一种能源高效的资源管理框架" 本文探讨了无人机辅助的多接入边缘计算系统（UAV-Assisted Multi-Access Edge Computing System），这是一种旨在提升网络容量并为无基础设施覆盖或有基础设施覆盖的移动用户提供服务的新型技术。随着物联网设备（IoTDs）的指数级增长，数据导向的应用程序数量也在不断增加。然而，由于IoTDs自身的计算能力和电源有限，处理本地收集的数据成为了一个挑战。为此，本文提出了一种创新的解决方案，即利用无人机作为移动边缘计算的载体。该系统的主要目标是降低IoTDs的能量消耗，包括本地计算能量和上行链路传输能量，以及无人机自身消耗的能量。为了实现这一目标，研究者建立了一个优化问题，该问题考虑了如何有效地分配计算任务、通信资源以及无人机的飞行路径，以最大化能源效率。在解决这个优化问题时，文章可能涵盖了以下关键知识点： 1. **无人机辅助边缘计算**：无人机作为移动边缘节点，可以在空中提供计算和存储资源，以减轻IoTDs的负担，同时扩大网络覆盖范围，特别是在偏远或灾难情况下。 2. **资源管理框架**：该框架可能涉及智能调度算法，用于决定哪些IoTDs的任务应由无人机处理，哪些应该本地处理，以及如何优化传输过程中的能量使用。 3. **优化问题**：这通常涉及到非线性规划、动态编程或启发式算法，旨在最小化总体能量消耗，同时确保服务质量（QoS）标准得到满足。 4. **能源效率**：这是设计过程中核心的考量因素，可能涉及到能量 harvesting 技术、低功耗计算策略以及动态功率调整等方法。 5. **物联网设备的挑战**：文章可能会讨论IoTDs的计算能力限制、电池寿命短以及处理大数据流的能力等问题，并提出相应的解决方案。 6. **通信技术**：在无人机和IoTDs之间的通信可能涉及无线通信技术，如5G、毫米波通信等，需要考虑传输距离、带宽和干扰等因素。 7. **路径规划**：无人机的飞行路径规划是另一个关键问题，需要考虑飞行时间、覆盖范围、能量消耗和通信质量。 8. **性能评估**：通过仿真或实验，研究可能对比不同的资源管理策略，展示所提出的框架相对于传统方法的优越性。这篇文章深入研究了无人机在边缘计算中的应用，以及如何构建一个能源高效的资源管理框架，以应对物联网设备的计算和能源挑战。通过综合考虑多个因素，它为未来的无人机辅助边缘计算系统提供了理论基础和技术指导。

UAV-Assisted Multi-Access Edge Computing

System: An Energy-Efﬁcient Resource Management

Framework

Madyan Alsenwi, Yan Kyaw Tun, Shashi Raj Pandey, Nway Nway Ei, and Choong Seon Hong

Department of Computer Science and Engineering, Kyung Hee University, 17104, Republic of Korea

Email: {malsenwi, ykyawtun7, shashiraj, nwayei, cshong}@khu.ac.kr

Abstract—Unmanned Aerial Vehicles (UAVs) have been de-

ployed to enhance the network capacity and provide services to

mobile users with and without infrastructure coverage. At the

same time, due to the exponential growth of the internet of things

devices (IoTDs), more and more data-oriented applications are

coming up. However, as IoTDs have limited computation capacity

and power, it is challenging to process collected data locally at the

IoTDs. Motivated by the aforementioned facts, we propose, in this

work, a UAV-assisted mobile edge computing system. Speciﬁcally,

the objective of this work is to minimize the energy consump-

tion of IoTDs, including local computation energy and uplink

transmission energy, and UAV energy consumption. To achieve

that, we formulate an optimization problem that optimizes the

task ofﬂoading, bandwidth resource allocation, local computation

resource allocation, and UAV computation resource allocation

subject to the latency constraint of all IoTDs and the limitation

of communication and computation capacity resources. Although

the formulated problem is a non-convex problem, it is composed

of convex subproblems, i.e., the formulated optimization problem

in the form of a multi-convex optimization problem. Therefore,

we decompose the formulated problem into convex subproblems

and then alternately solve them till converge to the desired

solution by using the Block Coordinate Descent (BCD) algorithm.

Simulation results show that the proposed approach signiﬁcantly

saves the system power consumption compared to other existing

schemes.

Index Terms—Unmanned aerial vehicles (UAVs), mobile edge

computing (mec), tasks ofﬂoading, resource allocation, block

coordinate descent (BCD).

I. INTRODUCTION

Due to the exponential growth of the internet of things

devices (IoTDs), such as metering devices and wearable de-

vices, more computation oriented applications, such as smart

farming, face recognition, virtual reality (VR), and augmented

reality (AR) are coming up in our life. These IoTDs are

low power devices and have limited computation capacity.

Therefore, it is difﬁcult for IoTDs to process their data locally.

Fortunately, deploying the mobile edge computing (MEC) con-

cept brings the computation resources nearer to the IoTDs and

This work was partially supported by Institute for Information & communi-

cations Technology Promotion (IITP) grant funded by the Korea government

(MSIT) (No.2015-0-00567, Development of Access Technology Agnostic

Next-Generation Networking Technology for Wired-Wireless Converged Net-

works), and by Institute for Information & communications Technology

Promotion(IITP) grant funded by the Korea government(MSIT) (No.2015-0-

00557, Resilient/Fault-Tolerant Autonomic Networking Based on Physicality,

Relationship and Service Semantic of IoT Devices). *Dr. CS Hong is the

corresponding author.

reduces the energy consumption of the IoTDs by ofﬂoading

their data to the MEC server. However, in some areas, e.g.,

smart farming in rural areas, disaster rescue operations, and

military operation, IoTDs will be far from the MEC services

and out of the coverage of the mobile infrastructure. In the

cases as mentioned above, it is difﬁcult for the IoTDs to use

the MEC services [1]–[4].

Unmanned aerial vehicles (UAVs) have been widely de-

ployed due to its high ﬂexibility, and low cost of deployment.

By deploying the MEC-enabled UAV, we can save the de-

ployment cost of the mobile infrastructures and provide the

remote MEC services to the IoTDs on demand. Motivated

by these facts, we propose an energy-efﬁcient UAV-assisted

edge computing system in this work. Recently, research works

focusing on UAV are gaining attention in both academia

and industry. For example, authors in [5] proposed a UAV-

enabled MEC system, where a UAV can power the IoTDs

by leveraging the wireless power transfer technology. They

modeled the system as an optimization problem that optimizes

the computing resource allocation, IoTDs association, wireless

power duration, UAV hovering time, and the service sequence

of the IoTDs while minimizing the energy consumption of the

UAV. The work in [2] proposed a UAV-MEC system consid-

ering the physical layer security. A non-convex optimization

problem for secure UAV-MEC system was formulated, with

the objective is to minimize the system energy, and then

transformed to convex problems. In [6], authors used a UAV

as an MEC server to process ofﬂoaded tasks from users.

They maximized the ofﬂoaded bits from users to the UAV by

optimizing UAV trajectory, uploading power of users, and user

association. In [7], authors studied the trajectory optimization

in the cellular-enabled UAV communication systems, while

authors in [8] maximized the throughput of mobile users in

UAV-enabled OFDMA systems by optimizing UAV trajectory

and resource allocation. The work in [9] has proposed an

optimization problem that jointly optimizes the trajectory and

ofﬂoading tasks from users to the UAV, where minimizing

the delay experienced by the mobile devices. The study in

[10] focused on minimizing the transmit power of UAVs by

optimizing the altitudes of UAVs. The K Means clustering

algorithm was leveraged to ﬁnd the optimal altitude of each

UAV.

In this work, we introduce an energy-efﬁcient UAV-assisted

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