一个基于Pareto的弹性分布式PSO,用于边缘无人机在飞行物联网中的部署优化
Sabrina Zerrougui1, Sofiane Zaidi1, Carlos T Calafate2
1Department of Mathematics and Computer Science, Research Laboratory on Computer Science's Complex Systems (ReLa(CS)2), University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了Pareto-PSO用于优化边缘启用无人机 (UAV) 部署. 帕雷托-PSO通过最大限度地覆盖,同时最大限度地减少飞行物联网的延迟和能源消耗来增强数据收集.
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 网络工程 网络工程
背景情况:
- 粒子集群优化 (PSO) 是一种优化无人机部署的常用技术.
- 现有的方法经常在动态环境中与多目标优化作斗争.
研究的目的:
- 提出一个框架,以优化边缘启用无人机部署使用帕雷托-PSO.
- 为了解决数据收集场景,需要自主无人机操作和机载分布式优化.
主要方法:
- 使用帕雷托-PSO进行无人机部署多目标优化的一种新框架.
- 自主无人机在机上执行分布式多目标PSO.
- 使用融合时间,吞吐量和覆盖面积指标进行绩效评估.
主要成果:
- 巴雷托-PSO实现了最高的吞吐量和最大的覆盖范围.
- 该方法证明了适度和可扩展的收时间.
- 在帕雷托-PSO中,向量值的客观处理被证明是有利的.
结论:
- 拟议的帕雷托-PSO框架为实时,可扩展和能源意识的边缘无人机部署提供了有效的解决方案.
- 这种方法非常适合动态的飞行物联网环境.
- 同时优化覆盖范围,延迟和能源消耗是可行的和有益的.
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