基于MARL的水下无线传感器网络的电源管理和性能优化
1School of Computer Science and Engineering, Guangdong Ocean University, Yangjiang, China.
PloS one
|March 6, 2026
概括
本研究介绍了一种使用多代理强化学习的智能算法,以优化水下无线传感器网络. 该方法通过智能地管理节点资源和功耗来提高网络容量,可靠性和寿命.
科学领域:
- 海洋技术 海洋技术
- 计算机科学 计算机科学
- 网络工程 网络工程
背景情况:
- 水下无线传感器网络 (UWSNs) 由于复杂的通道和有限的资源而面临性能下降.
- 现有的方法难以平衡能源消耗,传输可靠性和网络寿命.
研究的目的:
- 为优化UWSN性能提出一个智能算法.
- 为了减少能源消耗,提高可靠性和延长网络寿命.
主要方法:
- 正式化了UWSN优化作为一个部分可观测的马尔科夫决策过程.
- 应用多代理强化学习与团队奖励功能 (公平重复使用奖励,生存时间处罚).
- 为节点开发了一个分布式智能电源管理方案.
主要成果:
- 在异质情景中,实现了245.68kb的网络容量和1.85的公平重复使用指数.
- 保持低通信延迟 (6.18时段),节点故障为5%.
- 在动态环境中表现出强性,维持网络容量超过32,045kb,能源效率为0.4kb/J.
结论:
- 拟议的多代理强化学习算法显著提高了UWSN的稳定性和性能.
- 这种方法为海洋监测应用提供了有效的通信支持.
- 智能电源管理可以在具有挑战性的水下条件下提高网络寿命和可靠性.
相关概念视频
Maximum Power Transfer
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
By substituting the entire circuit with...
Marine Microbial Ecology
Marine microbial ecosystems are shaped by distinct physicochemical limits, including high salinity, low nutrient availability, and fluctuating oxygen levels. These conditions favor smaller microbial cell sizes, which maximize their surface-to-volume ratio for efficient nutrient uptake.Microbial activity and community composition are closely linked to biogeochemical cycles, particularly in dynamic environments like estuaries, where halotolerant microbes thrive in response to variable salinity...
