[1]江 雪,赵 亮.多无人机辅助移动边缘计算中的轨迹优化[J].计算机技术与发展,2023,33(05):110-115.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 017]
　JIANG Xue,ZHAO Liang.Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing[J].,2023,33(05):110-115.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 017]
点击复制

多无人机辅助移动边缘计算中的轨迹优化()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年05期
页码:
110-115
栏目:
移动与物联网络
出版日期:
2023-05-10

文章信息/Info

Title:
Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing
文章编号:
1673-629X(2023)05-0110-06
作者:
江 雪1 ; 赵 亮2
1. 南京邮电大学 物联网学院,江苏 南京 210003;
2. 杭州昊舜视讯科技有限公司,浙江 杭州 311100
Author(s):
JIANG Xue1 ; ZHAO Liang2
1. School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. Hangzhou Haoshun Vision Technology Co. ,Ltd. ,Hangzhou 311100,China
关键词:
移动边缘计算; 多无人机; 加权能耗; 轨迹优化; 卸载决策参数
Keywords:
mobile edge computing;  multi unmanned aerial vehicles;  weighted energy consumption;  trajectory scheduling;  offloaddecision parameter
分类号:
TP39;TN925
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 05. 017
摘要:
在无人机辅助移动边缘计算网络中,优化无人机的飞行轨迹可以显著提升无线网络的各项性能指标。 该文主要以加权最小化无人机的飞行能耗和接收卸载任务的能耗为目标,考虑满足无人机自身的机械特性和多无人机之间飞行轨迹需满足碰撞避免的约束条件,协同优化多架无人机的飞行轨迹和无人机与地面设备之间的卸载决策参数。 建立的基于能耗最小化的多无人机飞行轨迹的优化问题中,目标函数非线性,约束条件非凸。 针对这些问题,通过引入辅助变量转化非凸的优化条件,并通过连续凸优化的方法转化非线性的优化问题求解。 仿真结果表明,所提多无人机的轨迹优化算法,较好地优化了所有无人机的飞行轨迹,在保证所有地面设备任务卸载完成的前提下明显改善了无人机的能耗性能。
Abstract:
In the multi unmanned aerial vehicles ( UAV) assisted mobile edge computing ( MEC) ,optimizing the trajectory of the UAV isof great significance for improving the performance of wireless networks. In this paper, a weighted flight energy consumption andreceiving offload task energy consumption minimization problem is formulated to optimize the trajectory scheduling of UAVs and offloaddecision parameters, where both the multi - UAV mechanical property and collision avoidance mechanism are considered. For thenonlinear optimization problem and non-convex constraint conditions,we develop auxiliary variables to relax the non-convex constraintconditions and the successional convex optimization method. Simulation results demonstrate that the proposed trajectory optimizationalgorithm for the multi-UAV can obtain excellent UAV trajectories while reducing the consumption energy of UAVs.

相似文献/References:

[1]鲁 伟,宋荣方.基于模拟退火的多核多用户任务卸载调度[J].计算机技术与发展,2021,31(06):76.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 014]
　LU Wei,SONG Rong-fang.Multi-core Multi-user Task Offloading Scheduling Based onSimulated Annealing Algorithm[J].,2021,31(05):76.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 014]
[2]胡 恒,金凤林 *,谢 钧,等.设备间任务依赖的最佳卸载决策和资源分配[J].计算机技术与发展,2022,32(08):82.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 014]
　HU Heng,JIN Feng-lin*,XIE Jun,et al.Optimal Offloading Decision and Resource Allocation of Inter-devices Task Dependency[J].,2022,32(05):82.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 014]
[3]阳 柳,章立群,林晓勇.移动边缘计算中基于贡献度激励的端池化解决方案[J].计算机技术与发展,2024,34(03):83.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 013]
　YANG Liu,ZHANG Li-qun,LIN Xiao-yong.A Solution of Terminal Pooling Based on Contribution Emulated in Mobile Edge Computing[J].,2024,34(05):83.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 013]
[4]龚亮亮,张 影,张俊尧,等.基于深度强化学习的任务卸载和资源分配优化[J].计算机技术与发展,2024,34(04):116.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 018]
　GONG Liang-liang,ZHANG Ying,ZHANG Jun-yao,et al.Joint Optimization of Task Offloading and Resource Allocation Based on Deep Reinforcement Learning[J].,2024,34(05):116.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 018]

更新日期/Last Update: 2023-05-10