在基于多载波的无线供电传感器网络中,信息最小化的时代
Juan Sun1, Jingjie Xia1, Shubin Zhang2
1School of Computer and Data Engineering, NingboTech University, Ningbo 315100, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
这项研究使用深度强化学习来最大限度地减少无线传感器网络中的信息延迟. 这种新的方法减少了信息时代的加权平均值,以更好地交付数据.
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:
- 无线供电传感器网络 (WPSNs) 在及时提供信息方面面临着挑战.
- 优化数据传输和能源传输对于网络效率至关重要.
研究的目的:
- 在WPSNs中最小化信息时代 (WAoI) 的长期平均加权和.
- 开发一个有效的算法来平衡数据传输和能量传输.
主要方法:
- 制定了这个问题作为一个多阶段的随机优化程序.
- 应用Lyapunov优化,将问题分解为每个时间块的确定性子问题.
- 利用无模型的深度强化学习 (DRL) 来解决这些子问题.
主要成果:
- 与现有方法 (DQN,贪算法) 相比,拟议的基于DRL的算法显著降低了WAoI.
- 该算法有效地减轻了单个传感器的过度即时信息时代 (AoI).
- 在WPSNs中,在及时提供信息方面表现出卓越的表现.
结论:
- 新的Lyapunov优化和DRL方法为减少WPSN中的WAoI提供了有效的解决方案.
- 这种方法为增强无线传感器网络的实时监控提供了实用策略.
- 这些发现有助于在资源有限的网络中实现有效数据管理的进步.
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