一个基于FANET中的二元鱼优化算法的集群方案
Yonghang Yan1,2, Xuewen Xia1, Lingli Zhang3
1School of Computer and Information Engineering, Henan University, Kaifeng 475004, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种新的无人机网络集群方案,使用二进制鱼优化算法 (BWOA). 基于BWOA的方法提高了飞行特设网络 (FANET) 的能源效率和网络寿命.
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:
- 无人驾驶飞行器 (UAV) 越来越多地被用于各种应用,这导致了多个UAV网络的发展,也被称为飞行特设网络 (FANET).
- 通过聚类有效管理FANET对于优化能源消耗,延长网络寿命和提高可扩展性至关重要.
- 无人机固有的局限性,如能源限制和高流动性,对强大的集群通信网络构成重大挑战.
研究的目的:
- 为无人机集群提出一个高效的集群方案,利用二进制鱼优化算法 (BWOA).
- 解决移动无人机网络中能源效率和网络寿命的挑战.
- 提高FANET的可扩展性和通信网络能力.
主要方法:
- 根据网络带宽和节点覆盖限制计算最佳集群数量.
- 使用二进制优化算法 (BWOA) 选择集群头.
- 实施基于距离的聚类方法,将无人机分成组.
- 建立一个集群维护策略,以持续提高网络效率.
主要成果:
- 拟议的基于BWOA的集群方案在与现有的二进制粒子集群优化 (BPSO) 和K-means算法相比显示出更高的性能.
- 在减少能源消耗方面观察到显著的改善.
- 该计划有效地提高了无人机集群的整体网络寿命.
结论:
- 二元优化算法为无人机网络中的集群提供了有效的解决方案.
- 拟议的方案为管理FANET提供了一个有希望的方法,平衡能源效率和网络寿命.
- 这项研究有助于推进可扩展和可持续的多无人机通信系统.
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