基于近接政策优化的任务卸载框架,用于使用无人机辅助的WSN进行智能灾害监测
C N Vanitha1, P Anusuya1, Rajesh Kumar Dhanaraj2
1Department of Information Technology, Karpagam College of Engineering, Coimbatore, India.
MethodsX
|July 18, 2025
概括
这项研究引入了一个新的AI框架,ETORL-UAV,用于无人机 (UAV) 辅助的无线传感器网络 (WSN). 它显著提高了任务卸载成功,网络寿命,并在灾难场景中降低了能源消耗.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 无线通信无线通信
背景情况:
- 无人机对于无线传感器网络 (WSN) 在灾难监测中至关重要.
- 关键的挑战包括有限的能量,动态的任务分配和无人机的轨迹优化.
研究的目的:
- 提出一个新的框架,即ETORL-UAV,用于在无人机辅助的WSN中进行节能任务卸载.
- 通过强化学习在边缘启用WSN中增强无人机操作.
主要方法:
- 开发了ETORL-UAV,集成基于近接政策优化 (PPO) 的强化学习.
- 实施了多目标奖励模型,以平衡能量,任务成功和网络寿命.
主要成果:
- ETORL-UAV实现了高达9.3%的任务卸载成功率.
- 网络寿命有了18.75%的改善,能源消耗减少了27%.
- 在模拟中超越了五种最先进的方法.
结论:
- 对于无人机辅助的WSNs,ETORL-UAV提供了一个可扩展和可靠的解决方案.
- 该框架适用于现实世界的灾难响应部署.
- 强化学习有效地优化了WSN的无人机操作.
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