基于距离修正及灰狼优化算法对DV-Hop定位的改进

Improvement for DV-Hop Based on Distance Correcting and Grey Wolf Optimization Algorithm

无线传感器网络在实际应用中普遍存在节点布设不均匀的状况,导致使用经典DV-Hop(Distance Vector-Hop)方法实现节点定位存在较大的误差。为了提高定位精度,提出基于测距修正及灰狼优化算法的改进策略。首先,针对未知节点到每一目标锚节点的平均每跳距离校正值,采用一种相似路径搜索算法获得网络内一条最相似锚节点对间的多跳路径用于确定该值,以期提高未知节点到锚节点距离估计值的精度;进而,在使用Lateration算法获得未知节点初始位置后增加改进的灰狼群体智能算法优化步骤,以期提高定位精度。仿真实验结果显示,所提出的改进策略相对经典DV-Hop定位方法以及典型的改进方法,提高了对网络拓朴变化的鲁棒性,定位精度有了显著改善。

In the practical application of wireless sensor network,the node layout is not uniform usually,and that leads to relatively big node localization error of the classical DV-Hop(Distance Vector-Hop)method in use.In order to improve the localization accuracy,an improved strategy based on distance correcting and modified GWO(Grey Wolf Optimizer)algorithm is proposed.Firstly,aiming at the correction of the average distance per hop from an unknown node to each target anchor node,this strategy adopts a similar path searching algorithm to obtain the most similar path between anchor node pairs in the network to determine the value,so as to improve the accuracy of the estimated value of the distance from unknown node to anchor node.Then,a modified GWO algorithm is adopted to optimize the original positions of unknown nodes obtained by Lateration algorithm,in order to improve the final localization accuracy.The simulation results show that compared with the classical DV-Hop localization method and a typical improved method,our proposed strategy improves the robustness to network topology changes,and the localization accuracy has been significantly improved.