基于KNN的高级成本效益算法,用于在动态水下传感器网络中精确定位和能源优化
Nadia Shamshad1, Lei Wang1, Kiran Saleem1
1School of Software, Dalian University of Technology, Dalian, China.
Scientific reports
|January 17, 2025
概括
这项研究介绍了一种成本效益高的机器学习算法,使用K-Nearest Neighbors (KNN) 来改善水下传感器节点定位. 该方法显著减少了定位错误,能源使用和时间成本,提高了水下环境探索.
科学领域:
- 机器人技术和自主系统
- 机器学习 机器学习
- 海洋学 海洋学 海洋学
背景情况:
- 由于动态环境,水下传感器网络在本地化准确性,能源效率和运营成本方面面临重大挑战.
- 现有的方法经常与水下声学和节点移动性的复杂性作斗争,导致性能降低.
研究的目的:
- 开发和评估基于K-Nearest Neighbors (KNN) 的成本高效的机器学习算法,以优化使用传感器节点的水下环境采集.
- 在水下传感器网络中解决和最大限度地减少定位错误,能源消耗和时间成本.
- 为了提高在动态水下条件下传感器节点定位的准确性和效率.
主要方法:
- 基于K-Nearest Neighbors (KNN) 的机器学习算法被提出并实施用于水下传感器节点定位.
- 该算法旨在通过预测节点定向和绘制最短距离来优化上下文获取.
- 通过在水箱中的实时实验和使用 Ns-3.37 与 Aqua-sim 模型的模拟来验证有效性.
主要成果:
- 实现了99.98%的本地化准确性,显著改善了以前的方法.
- 将本地化错误率从4.59m降低到最小值 (具体值在摘要中标记为'[公式:参见文本]m').
- 证明了将本地化能耗降低到0.0045J,并将本地化时间成本率引入为0.06762s.
结论:
- 拟议的基于KNN的成本高效方法为增强水下传感器节点定位提供了创新和实用的解决方案.
- 该算法有效地减少了定位错误,能源消耗和时间成本,使水下环境探索更加可行.
- 该研究强调了KNN方法在动态水下场景中的实时实施和有效性.
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