量子群集优化的DV-Hop算法用于准确地定位无线传感器网络中的弱节点
Zahid Ullah Khan1, Hongyuan Gao2, Jingya Ma1
1College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China.
Scientific reports
|February 14, 2026
概括
本研究介绍了量子黄金鱼优化 (QGJO) 和量子头鱼优化 (QBSO),以提高无线传感器网络 (WSN) 的定位精度. QGJO-DV-HOP实现了16.79%的平均定位误差,表现优于QBSO-DV-HOP.
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:
- 精确的传感器节点定位在无线传感器网络 (WSN) 中至关重要.
- 传统的距离向量跳转 (DV-Hop) 方法与动态网络拓和不均的节点分布作斗争.
- 现有的DV-Hop算法表现出缓慢的融合,并且可以在局部最佳状态中卡住,从而妨碍精确的定位.
研究的目的:
- 为了提高WSN本地化在动态环境中的准确性和弹性.
- 解决传统DV-Hop算法的局限性,特别是其合速度和趋向于局部最佳的趋势.
- 引入新的优化技术,以改善WSN中的节点定位.
主要方法:
- 提出了两个改进的DV-Hop方法:量子黄金子优化 (QGJO) 和量子牛头鱼优化 (QBSO).
- 在GJO和BSO中集成了量子灵感功能,以防止过早的融合并保持算法真实性.
- 整合了群集情报通信功能和位置更新功能,以改进节点估计.
主要成果:
- QGJO-DV-HOP的平均定位误差为16.79%,标准偏差为2.59%.
- QBSO-DV-HOP的结果是平均定位误差为26.23%,标准偏差为4.38%.
- 模拟改变了网络覆盖范围,节点数,信标比例和拓转移,验证了算法性能.
结论:
- 与QBSO-DV-HOP和传统方法相比,QGJO-DV-HOP显著减少了WSN中的定位错误.
- 拟议的量子灵感优化技术在具有挑战性的WSN环境中提高了本地化准确性和稳定性.
- 集群智能和精细的位置更新的整合有助于更可靠的WSN节点本地化.
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