无人机辅助动态权重边缘计算卸载策略研究

王义君; 王雅出; SHAHDBatool; 缪瑞新

doi:10.11999/JEIT260054

无人机辅助动态权重边缘计算卸载策略研究

doi: 10.11999/JEIT260054 cstr: 32379.14.JEIT260054

长春理工大学电子信息工程学院长春 130022

基金项目: 国家自然科学基金(61771219)，吉林省自然科学基金(20250102227JC)

详细信息

作者简介:
王义君：男，教授，研究方向为通感一体无线网络通信及面向极端环境与特殊场景通信

王雅出：女，硕士生，研究方向为无人机边缘计算

SHAHDBatool：女，硕士生，研究方向为智能反射面资源分配

缪瑞新：男，讲师，研究方向为核磁共振成像及物联网技术

通讯作者:
王义君　wyjs-107@163.com

中图分类号: TN929.5
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出版历程
- 收稿日期: 2026-01-15
- 修回日期: 2026-03-17
- 录用日期: 2026-03-24
- 网络出版日期: 2026-04-22

Research on UAV-assisted Dynamic-weight Edge Computing Offloading Strategy

School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China

Funds: The National Natural Science Foundation of China (61771219), Jilin Provincial Natural Science Foundation (20250102227JC)

摘要

摘要: 针对无人机辅助移动边缘计算环境下计算资源受限、系统处理任务总开销过高问题，该文提出基于协同缓存自适应的分层多元宇宙优化(CCAH-MVO)算法优化卸载策略。首先，构建微云-边缘-本地3层网络架构，在无人机边缘服务器上预制缓存程序，采用细粒度部分卸载策略，并针对多无人机覆盖的终端设备制定无人机选择策略。然后，提出CCAH-MVO算法协同优化缓存、卸载和资源分配，并引入动态权重机制自适应平衡时延与能耗，得到最优卸载策略。仿真结果表明，所提策略的时延更优、能耗在裕度区内可控，综合性能优于基准卸载策略。
- 移动边缘计算 /
- 卸载决策 /
- 无人机选择策略 /
- 动态权重 /
- 多元宇宙
Abstract: Objective The increasing demands of the Internet of Things (IoT) for computational resources and real-time processing have highlighted the significance of Mobile Edge Computing (MEC). Traditional MEC relies on terrestrial base stations, resulting in coverage blind spots in remote or specialized environments. Unmanned Aerial Vehicle (UAV)-assisted MEC architectures exploit UAVs’ flexible deployment to expand service coverage. However, existing approaches for multi-terminal, multi-UAV scenarios often fail to optimize task offloading latency, system energy consumption, and adaptability to dynamic environments simultaneously. They also overlook optimal UAV selection when terminal devices are covered by multiple UAVs and lack adaptive mechanisms to adjust optimization objectives during task execution. This study addresses these challenges by integrating cooperative caching, offloading decision-making, and resource allocation strategies. Methods A three-tier microcloud-edge-terminal architecture is constructed, comprising a central cloud, multiple UAV edge servers with caching capabilities, and numerous mobile terminal devices. A cooperative caching mechanism reduces transmission delay during task execution. Task offloading adopts a fine-grained partial offloading mode, dividing complex tasks into dependent subtasks modeled through a Directed Acyclic Graph (DAG). The Cooperative Caching-Adaptive Hierarchical MultiVerse Optimizer (CCAH-MVO) algorithm is proposed. A hybrid coding scheme encodes offloading decisions, caching decisions, and resource allocation uniformly. A dynamic weight mechanism adaptively balances delay and energy consumption according to the system’s real-time energy state. Additionally, a UAV selection strategy is implemented for scenarios where terminals are covered by multiple UAVs. By simulating inter-universe material exchange and local refined search, the algorithm efficiently determines the optimal offloading strategy. MATLAB simulations validate the method under various experimental settings. Results and Discussions The simulation scenario involves 50 randomly distributed terminal devices and 5 UAVs in a 400 m × 400 m area. UAVs are deployed above terminal cluster centers, while terminals at cluster edges are simultaneously within the coverage of multiple UAVs (Fig. 5). The optimal UAV for each terminal is selected using the UAV selection function (Fig. 6), preventing resource bottlenecks and achieving balanced load distribution. In terms of delay performance, the CCAH-MVO algorithm maintains the lowest task delay across all task volumes, with a gradual increase as the number of tasks grows (Fig. 7). Delay under CCAH-MVO is consistently lower than that under fixed-weight strategies across the full task range, demonstrating the effectiveness of the dynamic adaptive mechanism in preserving low latency (Fig. 10). For energy consumption, differences among the algorithms are minor when task quantities are low. Under high task loads, the activation of the dynamic weight mechanism flattens the energy consumption curve (Fig. 8). When the number of tasks reaches 100, total energy consumption under CCAH-MVO is the lowest among all strategies and remains lower than the fixed-weight approach, reflecting effective control under critical energy conditions (Fig. 9). Regarding total system overhead, the CCAH-MVO algorithm consistently achieves the best performance. The gap with fixed-weight strategies widens when task numbers exceed 80, illustrating the dynamic weight mechanism’s collaborative optimization of delay and energy consumption (Fig. 11). Overall, by integrating the dynamic weight mechanism and balancing load through UAV selection, the CCAH-MVO algorithm effectively mitigates resource constraints and high task processing overhead in complex, dynamic UAV-assisted MEC environments. It ensures precise coordination between task delay and energy consumption across different load stages. Conclusions The proposed CCAH-MVO framework, incorporating a microcloud-edge-terminal architecture, cooperative caching mechanism, fine-grained partial offloading, dynamic weight adjustment, and UAV selection strategy, effectively addresses resource scheduling in complex multi-UAV MEC environments. Simulations show adaptive optimization of objectives, intelligent energy management, low latency, and reduced total system overhead, improving service stability and user experience. This research provides a practical solution for efficient UAV edge computing in dynamic environments. Future work will explore dynamic energy efficiency optimization and multi-node collaboration while maintaining low-latency performance.
- Mobile Edge Computing (MEC) /
- Offloading decision-making /
- UnmannedAerial Vehicle (UAV) selection strategy /
- Dynamic weight /
- Multi-Verse Optimizer algorithm (MVO)

HTML全文

图 1 系统模型图

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图 2 有向无环图

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图 3 初始开销矢量空间图

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图 4 混合编码结构图

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图 5 多无人机终端覆盖分布图

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图 6 无人机选择函数值对比图

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图 7 任务数量变化下不同算法时延性能的比较

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图 8 任务数量变化下不同算法能耗性能的比较

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图 9 多种策略的任务卸载总能耗

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图 10 多种策略的任务卸载总时延

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图 11 多种策略的任务卸载总开销

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1 协同缓存自适应的分层多元宇宙优化算法

输入：无人机集合$ \boldsymbol{N} $、终端设备集合$ \boldsymbol{M} $、任务集合$ \boldsymbol{L} $、程序集　合$ \boldsymbol{J} $、系统参数($ {Q}_{n} $,$ F_{n}^{l} $,$ F_{m}^{l} $,$ B $等)
输出：最优卸载策略(卸载决策$ \boldsymbol{A} $、缓存策略$ \boldsymbol{C} $、资源分配方案　$ \boldsymbol{R} $)
1. 初始化参数：迭代次数$ {T}_{\max } $、种群规模$ {P}_{\text{size}} $、裕度区阈值　$ \delta =0.8 $；
2. 采用K-means++聚类确定无人机悬停位置；
3. FOR每个宇宙$ i $：
4. 　生成混合编码向量$ {\boldsymbol{V}}_{i} $，满足缓存约束；
5. 　计算个体适应度值$ {H}_{L}\left({\boldsymbol{V}}_{i}\right)=\displaystyle\sum \nolimits_{i=1}^{k}{H}_{l} $，得到最优宇宙；
6. END FOR
7. WHILE $ t\leq {T}_{\max } $：
8. 　更新宇宙膨胀率；
9. 　根据$ {T}_{l}/{T}_{\text{loc}}/\sqrt{{\left({T}_{{l}}/{T}_{\text{loc}}\right)}^{2}+{\left({E}_{{l}}/{E}_{\text{loc}}\right)}^{2}} $调整$ \beta $；
10. 计算选择矩阵$ \boldsymbol{S} $；
11. FOR每个宇宙$ i $：
12. 　根据式(38)、式(39)、式(40)更新$ a_{k,{n}}^{\text{new}} $, $ c_{j,{n}}^{\text{new}} $, $ b_{m}^{\text{new}} $，更　　　新当前宇宙；
13. 　修复缓存容量和任务依赖；
14. END FOR
15. FOR每个宇宙$ i $：
16. 　重新计算更新后适应度值；
17. END FOR
18. $ t=t+1 $；
19. END WHILE

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表 1 仿真参数

仿真参数	参考数值
无人机数量N	5
终端设备数量M	50
信道总带宽B	20 MHz
终端设备传输功率$ {P}_{m} $	0.2 W
噪声功率密度$ {N}_{0} $	–168 dBm/Hz
参考信道增益$ {h}_{0} $	–60 dB
无线回程链路容量$ {R}_{\mathrm{b}} $	100 Mbps
覆盖角$ \theta $	$ \text{π} /6 $
每个子任务数据量$ D_{m}^{\text{in}} $	$ \left[0.2{,}0.4\right] $ MB
任务所需CPU周期数$ {W}_{m} $	$ \left[1.0{,}1.6\right] $ Giga-cycle
终端设备计算能力$ F_{m}^{l} $	0.5 GHz
无人机计算能力$ F_{n}^{l} $	$ \left[10{,}15\right] $ GHz
无人机飞行速度$ \upsilon $	15 m/s
无人机传输功率$ P_{\mathrm{u}} $	0.6 W
能耗系数κ	$ 1.0\times {10}^{-27} $
无人机存储容量$ {Q}_{n} $	300 MB

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