在混合雾计算中使用遗传算法来优化任务卸载,用于无人机互联网
Mohamed Amine Attalah1, Sofiane Zaidi2, Naçima Mellal3
1Department of Electronics, University Center of Tipaza, Tipaza 42000, Algeria.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究介绍了GA Hybrid-Fog,这是一种优化无人机互联网 (IoD) 中任务卸载的新策略. 它通过智能地将任务从无人驾驶飞行器 (UAV) 卸载到雾资源,大大减少了延误.
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:
- 无人机互联网 (IoD) 面临着由于动态拓和间歇性连接而导致任务卸载的挑战.
- 对于减少任务卸载延迟的严格要求对于有效的 IoD 操作至关重要.
研究的目的:
- 为支持雾的 IoD 环境提出一个优化的任务卸载策略.
- 为了应对 IoD 任务卸载中高动态性和间歇性连接的挑战.
主要方法:
- 启发式遗传算法 (GA) 用于任务卸载优化.
- 混合雾计算技术是集成的,利用雾基站 (FBS) 和雾无人机 (FUAV).
- 这项名为GA混合雾的战略优化了从边缘无人机 (UAV) 卸载决策.
主要成果:
- GA Hybrid-Fog有效地优化了传输和雾计算延迟.
- 拟议的方法保证了对卸载任务的增强存储和处理能力.
- 实验结果表明,与现有的 IoD 技术相比,任务卸载延迟的显著改善.
结论:
- 在动态的IoD环境中,GA Hybrid-Fog为任务卸载提供了一种优越的方法.
- 将GA与混合雾计算集成,有效地减轻了延迟,并提高了资源利用率.
- 该战略为提高无人机网络性能提供了一个有希望的解决方案.
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