基于在无线传感器网络中跳跃自适应灰狼优化器的3D覆盖的Node协作策略
Minghua Wang1,2, Zhuowen Wu1, Bo Fan1
1School of Electrical Engineering, University of South China, Hengyang 421001, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了三维自信信息覆盖 (3DCIC) 模型和用于无线传感器网络 (WSN) 的跳跃自适应灰狼优化器 (HAGWO). 这些创新显著提高了3D覆盖性能,并优化了节点部署.
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:
- 覆盖率优化是无线传感器网络 (WSN) 的一个关键挑战.
- 三维 (3D) 覆盖模型对于现实的应用,如智能城市管理,至关重要.
- 现有的模型往往无法在复杂的3D环境中最大化覆盖效率.
研究的目的:
- 提出一个新的三维可靠信息覆盖 (3DCIC) 模型,以加强WSN覆盖范围.
- 使用先进的元启发算法开发一个优化的节点部署策略.
- 提高3D WSNs的整体覆盖性能和效率.
主要方法:
- 该研究引入了三维自信信息覆盖 (3DCIC) 模型,利用多节点合作信息重建.
- 为节点部署优化而开发了一个跳跃自适应灰狼优化器 (HAGWO),结合了自适应和基于对立的学习.
- 提出的HAGWO算法是基于灰狼优化器 (GWO) 的,并应用于优化节点在3D空间中的位置.
主要成果:
- 与传统的二元球形模型相比,3DCIC模型显著改善了覆盖范围.
- 在四面体,六面体和八面体部署下,覆盖范围分别为2.78x,4.41x和4.00x.
- 哈格沃算法在优化节点部署以获得更高覆盖率方面被证明是有效的,并且在经典测试函数上表现良好.
结论:
- 拟议的3DCIC模型有效地增强了3D WSNs中的传感器节点的感知领域.
- 哈格沃算法提供了一种有效的方法来优化3D WSNs中的节点部署,从而大幅改善覆盖范围.
- 这项研究在3D WSN覆盖率优化方面取得了重大进展,用于智能城市管理等应用.
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