适应性上下文记忆网络,以提高海底物联网的通信和效率
1Department of IT, A. V. C. College of Engineering, Mayiladuthurai, 609305, Tamilnadu, India. padmavathi@avccengg.net.
Scientific reports
|December 9, 2025
概括
本研究介绍了适应性上下文记忆网络 (ACMN),以克服水下物联网 (IoUT) 的挑战. ACMN提高了可靠性,并节省了水生环境中的能量,以改善数据采集和交互.
科学领域:
- 海洋技术 海洋技术
- 网络工程 网络工程
- 数据科学数据科学数据科学
背景情况:
- 水下物联网 (IoUT) 对于水上导航和调查至关重要,但面临着重大挑战.
- 关键问题包括信号衰减,高延迟,低带宽,以及在水下环境中的大量能源消耗.
研究的目的:
- 提出一个新的框架,即适应性上下文记忆网络 (ACMN),以应对 IoUT 系统中固有的挑战.
- 提高水下数据采集和交互的可靠性和能源效率.
主要方法:
- 推出了自适应式上下文记忆网络 (ACMN),具有适应不断变化的水下条件的触摸界面.
- 开发了一个自适应调制优化 (AMO) 算法,以尽量减少信号退化和数据扭曲.
- 拟议的能源意识强化学习 (EARL) 基于实时网络反的自适应路由决策.
主要成果:
- 该ACMN框架显示在IoUT操作中可靠性增加和节能.
- AMO算法有效地减少了信号退化,并防止了数据扭曲.
- EARL增强了路由适应性,优化了动态水下环境中的网络性能.
结论:
- 集成的ACMN,AMO和EARL为IoUT提供了一个强大的和自主架构.
- 这一进步有助于在海洋生物学,海洋监测和水下基础设施监测方面取得重大改进.
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