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基于和度和Enhanced Grey Wolf优化的无线传感器网络的高效充电策略

P Neelagandan¹, S Balaji²

¹Department of Mathematics, Vellore Institute of Technology, 600127, Chennai, Tamil Nadu, India.

Scientific reports

|October 10, 2025

概括

在无线传感器网络 (WSN) 中优化充电器放置对于能源管理至关重要. 使用和度和增强灰狼优化的新混合方法显著提高了网络效率,达到97%.

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) 对现代应用至关重要,在长期能源管理方面面临挑战.
充电传感器比更换电池更好,因为成本效益和可持续性.
最佳的充电器放置对于最大限度地提高传感器覆盖率和最大限度地降低WSN中的部署成本至关重要.

研究的目的:

为WSN中的充电器放置问题提出一种新的混合优化框架.
提高无线传感器网络中能量补充的效率和成本效益.
通过战略性充电器部署来确保最大的传感器覆盖范围和网络寿命.

主要方法:

一个混合优化框架,将图形理论和度方法与增强的灰狼优化算法相结合.
和度方法用于识别传感器组,减少所需充电器的数量.
使用增强的灰狼优化算法来确定充电器部署的最佳空间位置.

主要成果:

拟议的混合方法实现了97%的网络效率,超过了传统技术.
与基于波形的方法 (Haar,Daubechies 2,Biorthogonal,Symlets 8) 和进化算法 (Raindrop,Blackhole) 相比,已经显著改进.
模拟证实了基于Enhanced Grey Wolf优化方法的实用WSN部署的稳定性和有效性.

结论:

混合优化框架为无线传感器网络中的充电器放置提供了卓越的解决方案.
增强的灰狼优化算法显著提高了能源补充效率和网络覆盖率.
拟议的方法为现实世界WSN部署提供了强大而有效的策略,提高了运营寿命.

关键词:

和度的程度.增强的灰狼算法灰狼算法充电器的最佳位置是充电器.无线充电器无线充电器

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