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Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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基于深度强化学习的耐延迟波动水下声学网络访问方法

Jinli Shi1, Kun Tian1, Jun Zhang1

  • 1School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
概括

这项研究引入了一种新的深度强化学习 (DRL) 方法,用于水下声学传感器网络 (UASN),可以解释随机延迟波动. 增强的DRL方法可以在具有挑战性的水下环境中提高网络访问和通信效率.

科学领域:

  • 水下声学传感器网络 (UASN) 是指水下声学传感器网络.
  • 深度强化学习 (DRL) 是一种深度强化学习.
  • 网络通信协议 网络通信协议

背景情况:

  • 声波在水中的传播会导致UASN的变量通信延迟.
  • 对于UASN的传统DRL方法与随机延迟波动作斗争,并具有较低的学习效率.

研究的目的:

  • 提出基于DRL的水下声学网络访问方法,可以抵抗延迟波动.
  • 在复杂的水下声环境中提高学习效率和决策.

主要方法:

  • 将延迟波动集成到适应性学习的DRL状态模型中.
  • 优化了双深Q网络 (DDQN) 结构,以提高性能.
  • 在不同的延迟波动条件下模拟了拟议的方法.

主要成果:

  • 与其他DRL方法相比,在趋同速度方面实现了29.3%和15.5%的平均改善.
  • 与TDMA和DOTS协议相比,显著提高了正常化吞吐量.
  • 已证明适应水下声链的随机延迟波动的适应性.

结论:

  • 拟议的DRL方法有效地解决了UASN的延迟波动.
关键词:
深度强化学习的学习.延迟波动的波动延迟时间媒体访问控制 媒体访问控制水下声学传感器网络的水下声学传感器网络.

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  • 优化的DDQN增强了学习和决策能力.
  • 该方法在水下声学网络的融合速度和吞吐量方面提供了卓越的性能.