计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (4): 74-84.doi: 10.11896/jsjkx.241000098

• 智能嵌入式系统 • 上一篇    下一篇

空天地一体化网络的无人机轨迹和计算卸载联合优化

陈奕天1, 童英华1,2   

  1. 1 青海师范大学计算机学院 西宁 810008
    2 青海师范大学省部共建藏语智能信息处理及应用国家重点实验室 西宁 810008
  • 收稿日期:2024-10-20 修回日期:2025-01-27 出版日期:2025-04-15 发布日期:2025-04-14
  • 通讯作者: 童英华(tyh_angel@126.com)
  • 作者简介:(1244339502@qq.com)
  • 基金资助:
    青海省应用基础研究项目(2023-ZJ-713)

Joint Optimization of UAV Trajectories and Computational Offloading for Space-Air-GroundIntegrated Networks

CHEN Yitian1, TONG Yinghua1,2   

  1. 1 School of Computer,Qinghai Normal University,Xining 810008,China
    2 State Key Laboratory of Tibetan Intelligent Information Processing and Application,Qinghai Normal University,Xining 810008,China
  • Received:2024-10-20 Revised:2025-01-27 Online:2025-04-15 Published:2025-04-14
  • About author:CHEN Yitian,born in 1999,postgra-duate.His main research interests include edge computing and air-heaven-ground integrated networks.
    TONG Yinghua,born in 1982,Ph.D,associate professor.Her main research interests include embedded system optimization and IoT system reliability.
  • Supported by:
    Qinghai Province Application Basic Research Program(2023-ZJ-713).

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摘要： 空天地一体化网络作为一种新兴的网络架构,近年来引起了广大研究者的关注,它能够很好地提高网络整体的服务质量。针对偏远地区的网络覆盖不全面,缺乏基本的网络基础设施的问题,提出了一种无人机和卫星共同收集任务的偏远地区空天地一体化网络框架,其中无人机与卫星为地面传感器提供边缘计算服务,云服务器为地面传感器提供云服务。由于无人机覆盖率、任务完成率和任务延迟都是影响系统性能的关键因素,因此对无人机轨迹和计算卸载进行联合优化,最大化无人机覆盖率和任务完成率,并且降低延迟。由于所提出的联合优化问题是一个混合非线性规划问题,因此设计了基于白鲸优化算法和沙猫群优化算法的双层优化算法,两层分别对无人机轨迹和计算卸载进行优化。实验结果表明,所提算法显著提高了多个无人机的覆盖率,且在计算卸载中有效提高了任务完成率,降低了任务的平均延迟。

关键词: 空天地一体化网络, 无人机, 边缘计算, 无人机轨迹, 计算卸载

Abstract: As an emerging network architecture,space-air-ground integrated network has attracted significant attention from researchers in recent years,and it can greatly improve the overall quality of service.Addressing the challenges of insufficient network coverage and the lack of basic infrastructure in remote areas,a space-air-ground integrated network framework is proposed in which unmanned aerial vehicles(UAVs) and satellites collaboratively collect tasks.In this framework,UAVs and satellites provide edge computing services for ground sensors,while cloud servers deliver cloud services.Given that UAV coverage,task completion rate,and task latency are critical factors influencing system performance,this study jointly optimizes UAV trajectory and computation offloading to maximize UAV coverage and task completion rate while minimizing latency.The proposed joint optimization problem is formulated as a mixed-integer nonlinear programming problem,therefore,a dual-layer optimization algorithm based on the Beluga Whale Optimization and Sand Cat Swarm Optimization is developed,with the two layers separately optimizing UAV trajectory and computation offloading.Experimental results show that the proposed algorithm significantly improves the coverage rate of multiple UAVs,effectively enhances the task completion rate,and reduces average task latency in computation offloading.

Key words: SAGIN, UAV, Edge computing, UAV trajectories, Computational offloading

中图分类号: 

  • TP393
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