MATOS： UAV swarm assisted moving-aware adaptive-parallel computing task offloading system

doi:10.11772/j.issn.1001-9081.2024101431

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (10): 3259-3269.DOI: 10.11772/j.issn.1001-9081.2024101431

• Advanced computing • Previous Articles

MATOS： UAV swarm assisted moving-aware adaptive-parallel computing task offloading system

Jian SUN¹^,², Wei ZHANG¹, Baoquan MA¹(), Zhuiwei WU¹, Xiaohuan YANG¹, Tao WU¹

^1.School of Computer Science and Engineering，North Minzu University，Yinchuan Ningxia 750021，China
^2.The Key Laboratory of Image and Graphic Intelligent Processing of the State Ethnic Affairs Commission （North Minzu University），Yinchuan Ningxia 750021，China

Received:2024-10-09 Revised:2025-03-06 Accepted:2025-03-10 Online:2025-03-26 Published:2025-10-10
Contact: Baoquan MA
About author:SUN Jian， born in 1982， Ph. D.， associate professor. His research interests include big data storage and management.
ZHANG Wei， born in 2000， M. S. candidate. His research interests include edge cache.
MA Baoquan， born in 1998， M. S. candidate. His research interests include mobile edge computing， big data storage and management.
WU Zhuiwei， born in 1999， M. S. candidate. His research interests include big data storage and management.
YANG Xiaohuan， born in 1998， M. S. candidate. Her research interests include big data storage and management.
WU Tao， born in 1998， M. S. candidate. His research interests include task scheduling.
Supported by:
National Natural Science Foundation of China(62062002);Ningxia Natural Science Foundation(2024AAC03192)

无人机群辅助的移动感知自适应并行计算任务卸载系统MATOS

孙鉴¹^,², 张伟¹, 马宝全¹(), 吴隹伟¹, 杨晓焕¹, 武涛¹

^1.北方民族大学计算机科学与工程学院，银川 750021
^2.图像图形智能处理国家民委重点实验室（北方民族大学），银川 750021

通讯作者: 马宝全
作者简介:孙鉴（1982—），男，山东烟台人，副教授，博士，CCF会员，主要研究方向：大数据存储与管理
张伟（2000—），男，宁夏银川人，硕士研究生，CCF会员，主要研究方向：边缘缓存
马宝全（1998—），男（回族），宁夏银川人，硕士研究生，CCF会员，主要研究方向：移动边缘计算、大数据存储与管理 Email:1945706641@qq.com
吴隹伟（1999—），男，湖南长沙人，硕士研究生，CCF会员，主要研究方向：大数据存储与管理
杨晓焕（1998—），女，山西平遥人，硕士研究生，CCF会员，主要研究方向：大数据存储与管理
武涛（1998—），男，山西大同人，硕士研究生，CCF会员，主要研究方向：任务调度。
基金资助:
国家自然科学基金资助项目(62062002);宁夏自然科学基金资助项目(2024AAC03192);宁夏自然科学基金资助项目(2022AAC03245)

Abstract

Abstract:

Unmanned Aerial Vehicle swarm （UAV swarm）， integrated with 5G networks， serves as a swarm flying tool carrying computational resources， providing additional computing power support for Mobile Edge Computing （MEC） networks. For semi-connected networks， where there are challenges such as lack of infrastructure computing power， massive task data， uneven distribution of mobile Internet of Things （IoT） devices， and complex communication scenarios using Orthogonal Frequency Division Multiple Access （OFDMA） technology， a Moving-aware Adaptive-parallel Task Offloading System （MATOS） was proposed. The system was comprised of a ground equipment layer， an Unmanned Aerial Vehicle （UAV） layer， and an edge computing layer， aiming to reduce task offloading latency and energy consumption， thus enhancing the task offloading success rate. In the proposed system， UAV swarm was utilized as Airborne Base Station （ABS） to handle task offloading and relay services. Firstly， to improve the task transmission quality between ground devices and the UAV swarm， a task collaborative collection mechanism was proposed by combining task attributes with mobile perception idea of regional service devices. Secondly， an Adaptive-parallel Genetic Ant Colony Optimization （AGACO） task offloading mechanism was proposed， and UAV swarm track planning idea was integrated to achieve load balancing for ABS and reduce task offloading latency. Finally， by jointly optimizing UAV swarm track planning， task offloading latency， and task offloading energy consumption， the task offloading success rate was improved. Experimental results show that MATOS reduces the flight energy consumption by 40% at most compared to energy efficient edge cloud architecture hieRarchical cloudlEt-baSed aERial Vehicle systEm （RESERVE）， Smart and Trusted Multi-UAV Task Offloading system （STMTO）， UAV Edge Computing IoT Network （UECIN）， Multi-UAV Assisted Offloading System （MAOS）， and Mobility-aware Online Task Offloading （MOTO）； compared with RESERVE， MATOS has the task offloading latency reduced by 38.8% at most， and the task offloading energy consumption reduced by 44.1% at most， which verifies the superiority of MATOS.

Key words: Mobile Edge Computing (MEC), Unmanned Aerial Vehicle swarm (UAV swarm), task offloading, load balancing, track planning, cluster parallel computing

摘要：

无人机群（UAV swarm）结合5G网络成为携带计算资源的集群飞行工具后可以为移动边缘计算（MEC）网络提供额外算力支持。在半连接网络中，针对基础设施算力稀缺、海量任务数据、移动物联网（IoT）设备分布不均和利用正交频分多址（OFDMA）技术进行通信的复杂场景等挑战，提出由地面设备层、无人机（UAV）层和边缘计算层构成的移动感知自适应并行计算任务卸载系统（MATOS），以降低任务的卸载时延和能耗，从而提升任务卸载的成功率。所提系统利用UAV swarm作为空中基站（ABS）完成任务卸载和任务中继服务。首先，为了提升地面设备与UAV swarm之间的任务传输质量，结合任务属性与区域服务设备移动感知思想提出任务协同收集机制；其次，提出自适应并行遗传蚁群优化（AGACO）任务卸载机制，同时结合UAV swarm航迹规划思想，使ABS负载均衡并降低任务卸载时延；最后，以UAV swarm航迹规划、任务卸载时延和任务卸载能耗为联合优化指标，提升任务卸载成功率。实验结果表明，与基于分层云团架构的飞行器系统RESERVE（hieRarchical cloudlEt-baSed aERial Vehicle systEm）、智能可信任务卸载系统（STMTO）和无人机边缘计算物联网网络（UECIN）、多无人机辅助卸载系统（MAOS）和移动感知在线任务卸载（MOTO）系统相比，MATOS在飞行能耗上最大降低了40%，在任务卸载时延上相较于RESERVE最大降低了38.8%，在任务卸载能耗上相较于RESERVE最大降低了44.1%，验证了MATOS的优越性。

关键词: 移动边缘计算, 无人机群, 任务卸载, 负载均衡, 航迹规划, 集群并行计算

CLC Number:

TP311.11

Jian SUN, Wei ZHANG, Baoquan MA, Zhuiwei WU, Xiaohuan YANG, Tao WU. MATOS： UAV swarm assisted moving-aware adaptive-parallel computing task offloading system[J]. Journal of Computer Applications, 2025, 45(10): 3259-3269.

孙鉴, 张伟, 马宝全, 吴隹伟, 杨晓焕, 武涛. 无人机群辅助的移动感知自适应并行计算任务卸载系统MATOS[J]. 《计算机应用》唯一官方网站, 2025, 45(10): 3259-3269.

Figures/Tables 15

Fig. 1 Architecture of three-layer mobile edge computing network

Tab. 1 Complexity analysis of AGACO

步骤	时间复杂度
初始化任务参数、任务队列、ABS计算资源	O（1）
初始化AGACO模型参数、任务种群	O（1）
初始化信息素矩阵	O（X²）
路径选择	O（P·T）
种群排序	O（P log P）
更新最优路径	O（P）
更新信息素矩阵	O（X²）
选择父代个体	O（P²）
交叉和变异操作	O（P·T）

Fig. 2 Flow of MATOS

Tab. 2 MATOS related parameters

参数	描述	值
M	UAV数	6~14
m	任务数	100~500
N	地面设备数	100~600
$d m, t$	任务数据大小	1~12 MB
$γ$	ABS芯片相关系数	2*2 GHz
$v$	UAV飞行速度	1 m/s
$p u s e n t$	UAV无线发射功率	0.1 W
$p u c o m p$	UAV运算功率	20 W
$ℏ$	空气密度	1.225 kg/m³
R	UAV旋翼半径	0.4 m
$δ$	剖面阻力系数	0.012
$b 0$	感应功率增量校正因子	0.1
W	UAV重量	20 N
$s'$	旋翼实度	0.05
$ζ$	机身等效板面积	0.002 m²
e	机身阻力比	0.001
$B$	通信带宽	31.24 MHz
$ε L o S$	LoS的路径损耗	1 dB
$ε N L o S$	NLoS的路径损耗	9 dB
f	载波频率	5 GHz
$r m a x$	短距离极限传输速率	100 Mbit/s
$R$	长距离极限传输速率	300 Mbit/s
$ω$	UAV允许接入数	1~55
$ψ$	计算资源下降因子	0.15
$a$	环境相关系数	5
$b$	环境相关系数	0.4
$β$	服务器空闲状态下的能量消耗比例	0.7

Tab. 2 MATOS related parameters

参数	描述	值
M	UAV数	6~14
m	任务数	100~500
N	地面设备数	100~600
$d m, t$	任务数据大小	1~12 MB
$γ$	ABS芯片相关系数	2*2 GHz
$v$	UAV飞行速度	1 m/s
$p u s e n t$	UAV无线发射功率	0.1 W
$p u c o m p$	UAV运算功率	20 W
$ℏ$	空气密度	1.225 kg/m³
R	UAV旋翼半径	0.4 m
$δ$	剖面阻力系数	0.012
$b 0$	感应功率增量校正因子	0.1
W	UAV重量	20 N
$s'$	旋翼实度	0.05
$ζ$	机身等效板面积	0.002 m²
e	机身阻力比	0.001
$B$	通信带宽	31.24 MHz
$ε L o S$	LoS的路径损耗	1 dB
$ε N L o S$	NLoS的路径损耗	9 dB
f	载波频率	5 GHz
$r m a x$	短距离极限传输速率	100 Mbit/s
$R$	长距离极限传输速率	300 Mbit/s
$ω$	UAV允许接入数	1~55
$ψ$	计算资源下降因子	0.15
$a$	环境相关系数	5
$b$	环境相关系数	0.4
$β$	服务器空闲状态下的能量消耗比例	0.7

Fig. 3 Service area division and UAV swarm track planning

Fig. 4 Impact of different number of tasks and UAVs on collaborative collection mechanism

Fig. 5 Changes in number of task collection load among UAVs with number of tasks under ten UAVs

Fig. 6 Impact of UAV flight altitude on transmission latency of tasks of different sizes

Fig. 7 Schematic diagram of AGACO path planning

Fig. 8 Impact of different population sizes， numbers of task， and UAV quantities on convergence of AGACO

Fig. 9 Impact of task size on computational energy consumption and latency under different numbers of tasks and UAVs

Tab. 3 Proposed design ideas of MEC systems

系统	设计思路
RESERVE^［18］	基于局部小型无人机终端、边缘大型无人机服务器与远程云组网方式，设计分散式任务卸载机制与博弈算法进行计算卸载
STMTO^［19］	基于无人机群以任务卸载能耗与时延最低收集任务，采用双向多对多任务拍卖机制进行任务卸载，基于用户位置进行UAV swarm航迹规划
UECIN^［23］	根据时间、日期等条件预测一定时间段内的地面用户数，进而提前更新无人机数；同时，无人机实时监控剩余能量，能量过低由新无人机替换。允许最大任务卸载时延，以减少无人机使用数
MAOS^［31］	基于全局搜索与局部搜索的进化算法，优化UAV swarm飞行路径与任务卸载时延
MOTO^［32］	提出移动性感知的任务卸载控制策略，通过长短期记忆优化任务卸载决策，通过D3QN（Dueling Double Deep Q-Network）解决边缘服务器计算负载不均衡的问题

Fig. 10 Comparison of track planning

Fig. 11 Comparison of task offloading latency and success rate

Fig. 12 Comparison of task offloading energy consumption

References 32

[1]	DEESE A S， JESSON J， BRENNAN T， et al. Long-term monitoring of smart city assets via internet of things and low-power wide-area networks［J］. IEEE Internet of Things Journal， 2021， 8（1）：222-231.
[2]	国际数据公司. IDC：2024年全球边缘计算投资将达 2 320亿美元［EB/OL］. （2024-06-04）［2024-08-11］. .
	International Data Corporation. IDC： global edge computing investment will reach 232 billion dollars in 2024［EB/OL］. （2024-06-04）［2024-08-11］. .
[3]	CHOUIKHI S， ESSEGHIR M， MERGHEM-BOULAHIA L. Computation offloading for industrial internet of things： a cooperative approach［C］// Proceedings of the 2023 International Conference on Wireless Communications and Mobile Computing. Piscataway： IEEE， 2023： 626-631.
[4]	QIN Z， HE S， WANG H， et al. Air-ground collaborative mobile edge computing： architecture， challenges， and opportunities［J］. China Communications， 2024， 21（5）：1-16.
[5]	DENG T， WANG Y， LI J， et al. Entropy normalization SAC-based task offloading for UAV-assisted mobile edge computing［J］. IEEE Internet of Things Journal， 2024， 11（15）：26220-26233.
[6]	LIU Z， CAO Y， GAO P， et al. Multi-UAV network assisted intelligent edge computing： challenges and opportunities［J］. China Communications， 2022， 19（3）： 258-278.
[7]	OUYANG Y， LIU A， XIONG N， et al. An effective early message ahead join adaptive data aggregation scheme for sustainable IoT［J］. IEEE Transactions on Network Science and Engineering， 2021， 8（1）： 201-219.
[8]	ZHANG L， WEN F， ZHANG Q， et al. Constrained multiobjective decomposition evolutionary algorithm for UAVs-assisted mobile edge computing networks［J］. IEEE Internet of Things Journal， 2024， 11（22）：36673-36687.
[9]	YANG X， ZENG Z， LIU A， et al. A decentralized trust inference approach with intelligence to improve data collection quality for mobile crowd sensing［J］. Information Sciences， 2023， 644： No.119286.
[10]	PENG K， YANG Y， WANG S， et al. Reliability-aware proactive offloading in mobile edge computing using Stackelberg game approach［J］. IEEE Internet of Things Journal， 2024， 11（9）：16660-16671.
[11]	XIANG B， ZHONG B， WANG A， et al. Edge computing collaborative offloading strategy for space-air-ground integrated networks［J］. Concurrency and Computation： Practice and Experience， 2024， 26（21）： No.e8214.
[12]	DU J， WANG J， SUN A， et al. Joint optimization in blockchain- and MEC-enabled space-air-ground integrated networks［J］. IEEE Internet of Things Journal， 2024， 11（19）：31862-31877.
[13]	QIU Q， LI L， XIAO Z， et al. Joint UAV deployment and task offloading in large-scale UAV-assisted MEC： a multiobjective evolutionary algorithm［J］. Mathematics， 2024， 12（13）： No.1966.
[14]	ZHANG Y， HOU X， DU H， et al. Joint trajectory and resource optimization for UAV and D2D-enabled heterogeneous edge computing networks［J］. IEEE Transactions on Vehicular Technology， 2024， 73（9）：13816-13827.
[15]	SUN L， LIU Z， NING Z， et al. Multi-agent Q-net enhanced coevolutionary algorithm for resource allocation in emergency human-machine fusion UAV-MEC system［J］. IEEE Transactions on Automation Science and Engineering， 2025， 22： 4473-4489.
[16]	QIN H， ZHENG Q， MEMMI G， et al. Deep residual learning-based enhanced JPEG compression in the internet of things［J］. IEEE Transactions on Industrial Informatics， 2021， 17（3）： 2124-2133.
[17]	RAHIM S， PENG L. Intelligent space-air-ground collaborative computing networks［J］. IEEE Internet of Things Magazine， 2023， 6（2）：76-80.
[18]	CHEN B， ZHOU H， YAO J， et al. RESERVE： an energy-efficient edge cloud architecture for intelligent multi-UAV［J］. IEEE Transactions on Services Computing， 2022， 15（2）： 819-832.
[19]	GUO J， HUANG G， LI Q， et al. STMTO： a smart and trust multi-UAV task offloading system［J］. Information Sciences， 2021， 573： 519-540.
[20]	GUO J， LIU A， OTA K， et al. ITCN： an intelligent trust collaboration network system in IoT［J］. IEEE Transactions on Network Science and Engineering， 2022， 9（1）：203-218.
[21]	ADIL M， JAN M A， LIU Y， et al. A systematic survey： security threats to UAV-aided IoT applications， taxonomy， current challenges and requirements with future research directions［J］. IEEE Transactions on Intelligent Transportation Systems， 2023， 24（2）： 1437-1455.
[22]	WANG Y， ZHU J， HUANG H， et al. Bi-objective ant colony optimization for trajectory planning and task offloading in UAV-assisted MEC systems［J］. IEEE Transactions on Mobile Computing， 2024， 23（12）：12360-12377.
[23]	GUO H， ZHOU X， WANG Y， et al. Achieve load balancing in multi-UAV edge computing IoT networks： a dynamic entry and exit mechanism［J］. IEEE Internet of Things Journal， 2022， 9（19）： 18725-18736.
[24]	SEID A M， BOATENG GO， MARERI B， et al. Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network［J］. IEEE Transactions on Network and Service Management， 2021， 18（4）： 4531-4547.
[25]	SHUAI J， CUI H， HE Y， et al. Dynamic satellite edge computing offloading algorithm based on distributed deep learning［J］. IEEE Internet of Things Journal， 2024， 11（16）：27790-27802.
[26]	刘红伟，潘灵，吴明钦，等. 一种FPGA集群轻量级深度学习计算架构设计及实现［J］. 电讯技术， 2024， 64（1）：14-21.
	LIU H W， PAN L， WU M Q， et al. Design and implementation of a lightweight deep learning computing architecture for FPGA cluster［J］. Telecommunication Engineering， 2024， 64（1）：14-21.
[27]	ZAFAR S， JANGSHER S， ZAFAR A. Federated learning for resource allocation in vehicular edge computing-enabled moving small cell networks［J］. Vehicular Communications， 2024， 45： No.100695.
[28]	LIU Q， ZHANG H， ZHANG X， et al. Improved DDPG based two-timescale multi-dimensional resource allocation for multi-access edge computing networks［J］. IEEE Transactions on Vehicular Technology， 2024， 73（6）：9153-9158.
[29]	SHARMA S， BALI R S. An edge computing framework for data analysis in intelligent transportation systems［C］// Proceedings of the 3rd International Conference for Innovation in Technology. Piscataway： IEEE， 2024： 1-6.
[30]	YAHYA M， CONZELMANN A， MAGHSUDI S. Decentralized task offloading and load-balancing for mobile edge computing in dense networks［J］. IEEE Communications Letters， 2024， 28（8）： 1954-1958.
[31]	WU P， XIAO F， HUANG H， et al. Load balance and trajectory design in multi-UAV aided large-scale wireless rechargeable networks［J］. IEEE Transactions on Vehicular Technology， 2020， 69（11）： 13756-13767.
[32]	DUAN S， LYU F， WU H， et al. MOTO： mobility-aware online task offloading with adaptive load balancing in small-cell MEC［J］. IEEE Transactions on Mobile Computing， 2024， 23（1）： 645-659.

MATOS： UAV swarm assisted moving-aware adaptive-parallel computing task offloading system

无人机群辅助的移动感知自适应并行计算任务卸载系统MATOS

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