面向大规模IoT系统的多无人机部署与协作卸载

doi:10.16182/j.issn1004731x.joss.24-0636

系统仿真学报 ›› 2025, Vol. 37 ›› Issue (1): 25-39.doi: 10.16182/j.issn1004731x.joss.24-0636

• 专栏：智能无人建模、仿真与应用 • 上一篇下一篇

面向大规模IoT系统的多无人机部署与协作卸载

黄智钦¹^,²^,³, 卢恬英¹^,²^,³, 陈哲毅¹^,²^,³

^1.福州大学计算机与大数据学院，福建福州 350116
^2.大数据智能教育部工程研究中心，福建福州 350002
^3.福建省网络计算与智能信息处理重点实验室（福州大学），福建福州 350116

收稿日期:2024-06-14 修回日期:2024-08-26 出版日期:2025-01-20 发布日期:2025-01-23
通讯作者: 陈哲毅
第一作者简介:黄智钦(2001-)，男，硕士生，研究方向为边缘计算、无人机部署、计算卸载。
基金资助:
国家自然科学基金(62202103);中央引导地方科技发展资金(2022L3004);福建省科技经济融合服务平台(2023XRH001);福厦泉国家自主创新示范区协同创新平台(2022FX5)

Multi-UAV Deployment and Collaborative Offloading for Large-scale IoT Systems

Huang Zhiqin¹^,²^,³, Lu Tianying¹^,²^,³, Chen Zheyi¹^,²^,³

^1.College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China
^2.Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China
^3.Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China

Received:2024-06-14 Revised:2024-08-26 Online:2025-01-20 Published:2025-01-23
Contact: Chen Zheyi

摘要/Abstract

摘要：

在大规模物联网（internet-of-things，IoT）系统中，无人机使能的移动边缘计算（mobile edge computing，MEC）可缓解终端IoT设备的性能限制。然而，由于不均匀的IoT设备分布与低效的问题求解效率，如何在大规模IoT系统中高效执行计算卸载面临着巨大的挑战。现有解决方案通常无法适应动态多变的多无人机场景，导致了低效的资源利用与过度的响应延迟。为解决这些重要挑战，提出了一种新型的面向大规模IoT系统的多无人机部署与协作卸载（multi-UAV deployment and collaborative offloading，MUCO）方法。设计了一种基于约束K-Means聚类的无人机部署方案，在提升服务覆盖率的同时保证覆盖均衡。设计了一种基于多智能体强化学习（multi-agent reinforcement learning，MARL）的多无人机协作卸载策略，将来自IoT设备的卸载请求进行拆分与分布式执行，进而实现高效的协作卸载。大量仿真实验验证了MUCO方法的有效性。与基准方法相比，MUCO方法在不同场景中平均可以取得约23.82%和28.13%的无人机部署性能提升，且能取得更低的时延和能耗。

关键词: 物联网, 移动边缘计算, 无人机部署, 计算卸载, K-Means聚类, 多智能体强化学习

Abstract:

In large-scale internet-of-things (IoT) systems, unmanned aerial vehicles (UAV) enabled mobile edge computing (MEC) can alleviate the performance constraints on end IoT devices. However, due to the uneven distribution of IoT devices and inefficient problem-solving, how to efficiently perform computation offloading in large-scale IoT systems is a major challenge. Existing solutions generally cannot fit into dynamic multi-UAV scenarios, causing inefficient resource utilization and excessive response delay. To address these important challenges, this paper proposes a novel multi-UAV deployment and collaborative offloading (MUCO) method for large-scale IoT systems. A UAV deployment scheme based on constrained K-Means clustering is designed to enhance service coverage while ensuring balanced coverage. A multi-UAV collaborative offloading strategy based on multi-agent reinforcement learning (MARL) is developed to split the offloading requests from IoT devices and conduct distributed execution, thereby realizing efficient collaborative offloading. Extensive simulation experiments validate the effectiveness of the proposed MUCO method. Compared to benchmark methods, the MUCO method can achieve an average improvement of about 23.82% and 28.13% improvement in UAV deployment performance in different scenarios,and can achieve lower latency and energy consumption.

Key words: internet-of-things, mobile edge computing, UAV deployment, computation offloading, K-Means clustering, multi-agent reinforcement learning

中图分类号:

TP393

黄智钦,卢恬英,陈哲毅 . 面向大规模IoT系统的多无人机部署与协作卸载[J]. 系统仿真学报, 2025, 37(1): 25-39.

Huang Zhiqin,Lu Tianying,Chen Zheyi . Multi-UAV Deployment and Collaborative Offloading for Large-scale IoT Systems[J]. Journal of System Simulation, 2025, 37(1): 25-39.

图/表 12

图1

图2

表1

仿真参数

仿真参数	数值
$D i [t s]$ / Mbit	[4,18] ^[22]
$S i [t s]$ /(cycles/bit)	[400,1 000]
$f u$ / MHz	[1 000, 1 200] ^[23]
$P u$ / mW	[400,600] ^[22]
$P u a v$ / mW	800
$f m a x$ / GHz	20
$B u$ / MHz	0.8
$B u a v$ / MHz	1
$ρ 0$	$- 10 - 5$ ^[24]
$N 0$	$- 2.5 × 10 - 15$ ^[7]
$λ 1$	0.7
$λ 2$	0.3

表1

图3

图4

图5

图6

图7

图8

图9

表2

图10

参考文献 27

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方法	决策时间/ms
MUCO	2.01
PPO	7.99
DDQN	8.00
SA	43.00

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面向大规模IoT系统的多无人机部署与协作卸载

Multi-UAV Deployment and Collaborative Offloading for Large-scale IoT Systems

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