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相关概念视频

Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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无人机辅助的基于集群的任务分配,用于移动人群传感,在一个空间-空中-地面-海洋集成网络中.

Yang Liu1, Yong Li1, Wei Cheng1

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种无人机辅助的基于集群的任务分配 (UCTA) 算法,用于集成网络中的移动人群传感 (MCS). 在动态环境中,UCTA算法提高了数据质量和覆盖范围.

关键词:
萨格辛 (SAGSIN) 的意思是说.集群算法就是集群算法.边缘计算是一种边缘计算.移动人群感觉越来越多任务分配 任务分配

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 数据科学数据科学数据科学

背景情况:

  • 移动人群传感 (MCS) 正在获得空间-空气-地面-海洋综合网络 (SAGSIN) 的吸引力.
  • 在SAGSIN中,动态的环境条件和可变的参与者能力对MCS中的数据质量和覆盖面构成了挑战.
  • 现有的MCS方法难以应对SAGSINs的复杂性.

研究的目的:

  • 为SAGSIN中的MCS提出一种新的无人机辅助集群式任务分配 (UCTA) 算法.
  • 在动态的SAGSIN环境中应对数据质量和覆盖范围的挑战.
  • 提高SAGSINs.内部MCS任务分配的效率.

主要方法:

  • 引入了边缘节点和一个三层层次的层次系统 (平台边缘集群参与者) 与无人机协助.
  • 开发了一个基于属性的边缘辅助集群算法来组织任务,减少通信和计算开销.
  • 采用一个贪的选择算法,以在集群中进行最佳的任务分配.

主要成果:

  • 拟议的UCTA算法在与三个基准算法相比显示出更高的性能.
  • 实现了通信开销和计算复杂性的显著降低.
  • 通过广泛的模拟,SAGSIN中MCS任务分配的提高效率得到了验证.

结论:

  • UCTA算法有效地提高了SAGSINs内的MCS数据质量和覆盖范围.
  • 层次结构和集群方法是算法的成功的关键.
  • 在复杂的集成网络中,UCTA为高效和有效的移动人群传感提供了一个有前途的解决方案.