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<p>In the aftermath of geological disasters, when ground base stations are damaged and users are spatially distributed across complex terrain, Unmanned Aerial Vehicles (UAVs) equipped with edge servers can serve as aerial base stations to provide services to ground users. However, the number of UAVs deployed, their positioning, and the matching between UAVs and users are crucial prerequisites for ensuring effective service delivery. Additionally, due to the limited energy of UAVs, balancing the task load and reducing path loss while ensuring communication coverage presents significant challenges. To address the optimization problem of task load balancing and three-dimensional (3D) deployment of multiple UAVs, this paper proposes a task-balanced clustering method for UAV horizontal positioning, combined with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm to optimize the vertical deployment of UAVs. First, the task loads carried by users and the distances between them are considered, and a weighted objective function is defined for clustering. The improved K-means algorithm is then applied to cluster ground users and determine the optimal horizontal positions of UAVs. Subsequently, the optimal vertical deployment height of UAVs is determined by minimizing the total intra-cluster path loss. Simulation results show that the proposed optimization scheme successfully reduces path loss and outperforms other comparable methods. Specifically, path loss is reduced by at least 7.5% for varying area sizes and by at least 6.2% for different numbers of users.</p>

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Three-dimensional server deployment optimization in multi-UAV-assisted edge networks

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • Jianhua Liu,
  • Guilin Yuan,
  • Bo Tang,
  • Jiajia Liu,
  • Xiaoguang Tu,
  • Xia Lei

摘要

In the aftermath of geological disasters, when ground base stations are damaged and users are spatially distributed across complex terrain, Unmanned Aerial Vehicles (UAVs) equipped with edge servers can serve as aerial base stations to provide services to ground users. However, the number of UAVs deployed, their positioning, and the matching between UAVs and users are crucial prerequisites for ensuring effective service delivery. Additionally, due to the limited energy of UAVs, balancing the task load and reducing path loss while ensuring communication coverage presents significant challenges. To address the optimization problem of task load balancing and three-dimensional (3D) deployment of multiple UAVs, this paper proposes a task-balanced clustering method for UAV horizontal positioning, combined with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm to optimize the vertical deployment of UAVs. First, the task loads carried by users and the distances between them are considered, and a weighted objective function is defined for clustering. The improved K-means algorithm is then applied to cluster ground users and determine the optimal horizontal positions of UAVs. Subsequently, the optimal vertical deployment height of UAVs is determined by minimizing the total intra-cluster path loss. Simulation results show that the proposed optimization scheme successfully reduces path loss and outperforms other comparable methods. Specifically, path loss is reduced by at least 7.5% for varying area sizes and by at least 6.2% for different numbers of users.