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通过BIC优化进行智能无人机定位,以最大限度地提高LPWAN覆盖范围和容量,在亚马逊郊区的环境中

Flávio Henry Cunha da Silva Ferreira¹, Miércio Cardoso de Alcântara Neto¹, Fabrício José Brito Barros¹

¹Institute of Technology, Federal University of Pará (UFPA), Belém 66075-110, Brazil.

Sensors (Basel, Switzerland)

|July 14, 2023

概括

这项研究优化了使用生物灵感算法的无人机基站放置,以最大限度地提高亚马逊城市森林的无线覆盖范围. 结果显示,物联网 (IoT) 应用程序的网络性能得到了改善.

科学领域:

无线通信系统无线通信系统
网络优化网络优化
机器人和自动化机器人与自动化

背景情况:

无人机 (UAV) 基站提供灵活的网络部署.
低功耗无线区域网络 (LPWAN),就像LoRa一样,适用于低数据速率的广泛覆盖.
优化无人机定位对于最大限度地覆盖复杂环境至关重要.

研究的目的:

为优化无人机阵列放置提供一种元启发式方法.
为了最大限度地覆盖使用无人机基站的无线通信系统的覆盖面积.
分析郊区,森林亚马逊城市环境中的性能.

主要方法:

应用低功耗无线区域网络 (LPWAN) 技术,特别是LoRa.
利用了三种生物启发计算 (BIC) 方法:子搜索 (CS),花粉算法 (FPA) 和基因算法 (GA) 用于无人机定位.
使用 MATLAB 模拟开发并验证了森林环境的实证传播模型.

主要成果:

优化的无人机定位被模拟为高范围的物联网-LoRa网络.
在森林地区 (SF 8-11) 开发了LoRa的实证传播模型.
对无人机的理论和测量传播模型进行了比较.

结论:

这种元启发式方法有效地优化了无人机阵列的位置,以提高无线覆盖范围.
开发的传播模型为亚马逊城市森林中的信号行为提供了更准确的表示.
这项研究有助于在具有挑战性的地形上有效部署基于无人机的通信网络.

关键词:

一个LOFT的东西.洛拉洛拉是什么意思生物启发的计算技术道建模道建模优化覆盖范围的优化无线传感器网络是无线传感器网络.

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