Journal of System Simulation ›› 2025, Vol. 37 ›› Issue (1): 25-39.doi: 10.16182/j.issn1004731x.joss.24-0636

• Special Column:Modeling,Simulation and Application for Intelligent Unmanned System • Previous Articles     Next Articles

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

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

  1. 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

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

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