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

Authors

Zhiqin Huang, College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China
Tianying Lu, College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China
Zheyi Chen, College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China

Abstract

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.

Recommended Citation

Huang, Zhiqin; Lu, Tianying; and Chen, Zheyi (2025) "Multi-UAV Deployment and Collaborative Offloading for Large-scale IoT Systems," Journal of System Simulation: Vol. 37: Iss. 1, Article 3.
DOI: 10.16182/j.issn1004731x.joss.24-0636
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss1/3

First Page

25

Last Page

39

CLC

TP393

Recommended Citation

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.

Corresponding Author

Chen Zheyi

DOI

10.16182/j.issn1004731x.joss.24-0636