The process of locating nodes is really a challenging problem in the field of wireless sensor networks. Wireless sensor network localization is commonly followed by the distance vector algorithm. All beacon nodes are currently using DV-Hop algorithms to locate the dumb node. On the other hand, the approximate distance from the dumb node to certain beacon nodes contains a significant error, resulting in a large finished dumb node localization problem. To improve localization error an efficient DV-Hop method on social learning class topper optimization for wireless sensor networks is implemented in this paper. The proposed algorithm reduces communication between unknown or dumb and beacon nodes by measuring the dimensions of all the beacons at dumb nodes. The network imbalance model is frequently used to show the applicability of the proposed approach in anisotropic networks. Simulations are performed on LabVIEW 2015 platform. The results show that our proposed method outperforms some existing algorithms in terms of computing time (2%), localization error (6.6%), and localization error variance (8.3%).

定位节点的过程在无线传感器网络领域确实是一个具有挑战性的问题。无线传感器网络定位通常遵循距离矢量算法。目前所有的信标节点都在使用 DV-Hop 算法来定位哑节点。另一方面，哑节点到某些信标节点的近似距离包含很大的误差，导致完成哑节点定位问题很大。为了改善定位误差，本文实现了一种有效的 DV-Hop 方法，用于无线传感器网络的社会学习类 topper 优化。所提出的算法通过测量哑节点上所有信标的维度来减少未知或哑节点与信标节点之间的通信。网络不平衡模型经常被用来展示所提出的方法在各向异性网络中的适用性。仿真在 LabVIEW 2015 平台上进行。结果表明，我们提出的方法在计算时间（2%）、定位误差（6.6%）和定位误差方差（8.3%）方面优于一些现有算法。