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首页> 外文期刊>Wireless personal communications: An Internaional Journal >Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks
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Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks

机译：改进了各向异性无线传感器网络中本地加权线性回归的DV-Hop算法

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摘要

The original DV-hop algorithm performs pretty well in isotropic Wireless Sensor Networks in which nodes distribute uniformly. However, the localization accuracy degrades severely in anisotropic networks caused by uneven nodal distribution or irregularity of deployment region. In this paper, we propose a novel DV-hop algorithm based on Locally Weighted Linear Regression (LWLR-DV-hop), in which kernel method is adopted to improve the localization accuracy by raising the weight of neighboring anchor nodes. In the simulation section, algorithms are evaluated within two deployments and three topologies: the regular and random deployments, the L-shaped, O-shaped and X-shaped topologies. As performance metrics, the Average Localization Error and the Cumulative Distribution Function are used. The results of simulation and experiment reveal that LWLR-DV-hop performs better than original DV-Hop in anisotropic networks of different topologies, in which localization accuracy is improved by about 40% on average.
机译：原始DV-Hop算法在各向同性无线传感器网络中执行得很好，其中节点均匀地分布。然而，本地化精度在不均匀的节点分布或部署区域的不规则性引起的各向异性网络中严重降低。在本文中，我们提出了一种基于局部加权线性回归（LWLR-DV-HOP）的新型DV-Hop算法，其中采用内核方法来通过提高相邻锚点节点的重量来提高定位精度。在仿真部分中，在两个部署和三个拓扑中评估算法：常规和随机部署，L形，O形和X形拓扑。作为性能指标，使用平均本地化误差和累积分布函数。仿真和实验结果表明，LWLR-DV跳在不同拓扑的各向异性网络中的原始DV跳，其中定位精度平均提高了约40％。

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