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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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一个基于模糊的群优化算法,用于在无线传感器网络中有效检测故障节点.

B Nagarajan1, Santhosh Kumar Svn2, M Selvi3

  • 1School of computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India. nagarajan.b@vit.ac.in.

Scientific reports
|November 11, 2024
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概括
此摘要是机器生成的。

本研究介绍了一种用于检测无线传感器网络 (WSN) 中传感器节点故障的新算法. 拟议的方法提高了故障检测准确性和网络性能,同时最大限度地降低了能源消耗和错误报警.

关键词:
故障检测 检测故障检测节点故障错误节点的错误优化优化 优化优化森隐藏马尔科夫模型的模型传感器节点是一个传感器节点.

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 网络工程 网络工程

背景情况:

  • 无线传感器网络 (WSN) 对于环境监测至关重要,但由于部署挑战和有限的资源,会遭受传感器节点故障.
  • 在WSN中的节点故障可能导致拓变化,通信中断,网络分区和数据传输错误.
  • 有效检测,诊断和恢复传感器节点故障对于WSN可靠性至关重要.

研究的目的:

  • 为无线传感器网络 (WSN) 提出一个有效的传感器节点故障检测算法.
  • 为了提高故障检测的准确性,减少错误报警和错误阳性,并优化能源消耗.
  • 为了增强WSN内有缺陷的传感器节点的定位.

主要方法:

  • 一个新的算法,将Poisson隐藏马尔科夫模型 (PHMM) 结合起来,用于故障检测和基于Fuzzy的群优化 (F-CSO),以改进本地化和参数优化.
  • 使用NS2模拟器实现和模拟,使用现实的参数来评估性能.
  • 与现有的最先进系统进行比较,以证明有效性.

主要成果:

  • 实现了89.5%的故障检测准确度,与现有方法相比显著改进.
  • 产量增加了19.53%,能源消耗减少了8.43%.
  • 与其他系统相比,显示了最小的延迟率和较低的假阳性率.

结论:

  • 拟议的基于PHMM和F-CSO的算法为检测WSN中的传感器节点故障提供了非常有效的解决方案.
  • 该方法显著提高了网络性能指标,包括精度,吞吐量和能源效率.
  • 这种方法提供了一个强大的策略,在面对节点故障时保持WSN完整性和可靠性.