动态雾节点放置优化使用适应性动态泡鱼优化实时物联网网络的动态雾节点位置优化.
Ashraf A Abu-Ein1,2, Obaida M Al-Hazaimeh1,3, Mohammed Tawfik4
1Department of Computer Networks and Cybersecurity, Faculty of Information Technology, Jadara University, Irbid, Jordan.
Scientific reports
|March 11, 2026
概括
本研究介绍了动态鱼优化算法 (D-POA),用于在动态环境中适应性雾节点的放置. D-POA 提高了连接性和覆盖范围,同时大大降低了移动成本,以提高物联网服务质量.
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:
- 物联网 (IoT) 设备的扩散需要高效的雾计算架构.
- 具有移动节点,故障和流量波动的动态环境对最佳的雾节点放置构成挑战.
- 维持低延迟处理和服务质量需要适应性策略来部署雾节点.
研究的目的:
- 为实时,自适应性雾节点重新定位引入一种新的生物灵感算法.
- 解决雾结位置的多目标优化问题,考虑连接,覆盖和移动成本.
- 在动态雾计算环境中尽量减少服务中断.
主要方法:
- 开发动态鱼优化算法 (D-POA),灵感来自鱼的行为.
- 制定雾结位置问题作为一个连续的多目标优化任务.
- 在各种动态网络场景中进行实验性评估.
主要成果:
- D-POA实现了97.8%的网络连接率和98.4%的区域覆盖率.
- 与基线算法相比,移动成本减少了38-57%.
- 证明了近线性可扩展性,在高达1000个节点的网络中保持超过96%的解决方案质量.
结论:
- 动态鱼优化算法 (D-POA) 提供了一个有效的解决方案,用于在动态物联网环境中适应性雾节点的放置.
- D-POA成功地平衡了勘探和开采,以实现最佳的实时重新配置.
- 该算法在连接性,覆盖范围和成本效益方面取得了显著的改进,超过了现有的方法.
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