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生物启发的算法用于在飞行特设网络中高效的集群和路由
Juhi Agrawal1, Muhammad Yeasir Arafat2
1School of Computer Science, University of Petroleum & Energy Studies, Prem Nagar, Dehradun 248007, India.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究介绍了HMAO,这是一种用于飞行特设网络 (FANET) 的混合算法. HMAO通过优化无人机集群头选择和路由来提高网络稳定性和数据传输.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 在飞行特设网络 (FANET) 中的无人驾驶飞行器 (UAV) 提出了独特的移动性挑战.
- 传统的集群和路由方法在动态FANET环境中难以稳定,资源效率和延迟.
研究的目的:
- 开发一种新的混合生物灵感算法,HMAO,用于在FANET中改进集群和路由.
- 在动态无人机网络中增强网络稳定性,数据传输可靠性和资源利用.
主要方法:
- 拟议的HMAO算法,将山地优化器 (MGO) 结合用于集群头 (CH) 选择和Aquila优化器 (AO) 用于路由.
- 对于稳定的CH选择,MGO考虑了UAV的能量,移动性,集群内距离和邻居密度.
- AO利用预测性移动性,负载平衡,故障耐受性和渡轮节点进行可靠的数据传输.
主要成果:
- 与现有方法相比,HMAO显示出优越的集群稳定性.
- 在数据包交付率和减少网络延迟方面观察到显著的改进.
- 使用HMAO技术实现了更低的能源消耗和更低的开销.
结论:
- 混合HMAO算法有效地解决了FANET中的集群和路由的挑战.
- 在动态无人机网络中,HMAO为稳定,高效和可靠的数据通信提供了强大的解决方案.
- 模拟结果验证了HMAO对传统方法的性能优势.
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