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传感器节点部署优化,以在WSN中实现连续覆盖.
1Department of Electrical Engineering, Hanyang University, Ansan 15588, Republic of Korea.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一种新的算法,速度缩放的自适应性搜索因子粒子群集优化 (VASF-PSO),以改善无线传感器网络 (WSN) 覆盖率. VASF-PSO方法增强了节点分布,减少了未覆盖的区域,取得了明显更好的结果.
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:
- 优化传感器节点覆盖是无线传感器网络 (WSN) 中的一个关键挑战.
- 传统的优化方法经常遭受过早的融合和低于最佳的解决方案,导致覆盖差距和节点分布不均.
- 现有的方法很难有效地平衡勘探和开发,以便有效地部署WSN.
研究的目的:
- 介绍一个新的优化算法,速度缩放的自适应性搜索因子粒子群优化 (VASF-PSO).
- 增强人口多样性,在WSN覆盖率优化中更有效地指导搜索过程.
- 为了减少未覆盖的区域,并提高整体网络覆盖性能.
主要方法:
- 开发速度缩放的自适应性搜索因子粒子群集优化 (VASF-PSO) 算法.
- 在VASF-PSO中整合动态机制,以改善人口多样性和搜索指导.
- 在各种WSN部署场景 (不同节点密度,传感范围和区域大小) 中评估VASF-PSO的广泛模拟.
主要成果:
- 与几种广泛使用的元启发算法相比,VASF-PSO算法表现出卓越的性能.
- 实现了更快的融合率和更好的全球勘探能力.
- 与基线技术相比,覆盖率显著提高,平均覆盖率高达14.71%.
结论:
- 拟议的VASF-PSO算法为优化WSN中的传感器节点覆盖率提供了一个强大而高效的解决方案.
- 集成到VASF-PSO中的动态机制有效地解决过早的融合和次优化解决方案.
- 该算法的可扩展性和改进的覆盖性能使其适合于实际的WSN部署.
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