在水下声学传感器网络中选择最佳路径和安全传输数据:基于LSTM的能源预测
Sathish Kaveripakam1, Ravikumar Chinthaginjala1
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
PloS one
|September 5, 2023
概括
本研究介绍了一种用于在水下声学传感器网络 (UASN) 中选择最佳路径和安全传输数据的新型模型. 该模型利用长短期内存 (LSTM) 来进行能源预测,并使用混合优化算法来提高通信效率.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 海洋学 海洋学 海洋学
背景情况:
- 水下声学传感器网络 (UASN) 面临通信中断,原因是声学通道的时间和空间变化.
- 现有的系统在通过自适应调制来减少干扰和提高通信质量方面扎.
研究的目的:
- 在UASN中提出一个最佳路径选择和安全数据传输模型.
- 在具有挑战性的水下环境中提高通信效率和质量.
主要方法:
- 基于能量,距离和链路质量 (通量) 的节点选择.
- 使用长短期记忆 (LSTM) 进行能量预测.
- 通过混合更新黑猩猩优化算法 (PUCOA) 选择最佳路径.
- 使用改进的Blowfish算法 (IBFA) 进行安全的数据传输.
主要成果:
- LSTM+PUCOA模型实现了高性能指标,包括90.85%的准确性,92.78%的精度和89.76%的F1得分.
- 与标准基准模型相比,表现出显著的改进.
- 实现了7.21%的低虚假阳性率 (FPR) 和10.20%的虚假阴性率 (FNR).
结论:
- 拟议的LSTM+PUCOA模型有效地解决了UASN中最佳路径选择和安全数据传输的挑战.
- 混合优化和基于LSTM的能源预测为水下声学通信提供了强大的解决方案.
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