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无人机辅助的物联网网络框架,具有混合深度增强和联合学习
Andreas Andreou1, Constandinos X Mavromoustakis2, Evangelos Markakis3
1Department of Computer Science, University of Nicosia, 46 Makedonitissas Avenue, 1700, Nicosia, Cyprus. andreou.andreas@unic.ac.cy.
Scientific reports
|October 23, 2025
概括
这项研究将无人机 (UAV) 部署与保护隐私的物联网网络联合学习 (FL) 整合在一起. 新的框架提高了数据吞吐量,减少了延迟,提高了整体网络效率.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 无人机 (UAV) 辅助的物联网 (IoT) 网络在优化无人机部署,整合联合学习 (FL) 和动态管理资源方面面临着挑战.
- 现有的研究往往将无人机安置优化与FL驱动的资源配置分开,限制了网络性能.
研究的目的:
- 为物联网环境中的多无人机网络提出一个统一的混合框架,该框架集成了无人机部署优化,保护隐私的FL和自适应性资源分配.
- 为了增强蜂覆盖,促进有效的数据收集,并解决动态网络条件.
主要方法:
- 开发了一个混合框架,利用深度强化学习 (DRL) 和FL.
- 引入了使用粒子集群优化 (PSO) 的多无人机网络形成 (MUNF) 算法,以提高信号对噪声比 (SNR).
- 实施了动态适应战略 (DAS) 与深度决定性政策梯度 (DDPG) 以优化资源配置.
主要成果:
- 在数据吞吐量方面实现了26%的增加.
- 网络延迟减少了18%.
- 与现有方法相比,显示出更稳定的SNR分布.
结论:
- 拟议的框架有效地解决了无人机辅助物联网网络的关键缺口,通过统一部署,FL和资源分配.
- 结果验证了在动态条件下网络效率,可扩展性和性能的显著改进.
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