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An Effective Area-Based Localization Algorithm for Wireless Networks

机译:一种有效的基于区域的无线网络定位算法

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Area-based localization algorithms use only the position of some reference nodes, called anchors, to estimate the residence area of the remaining nodes. Existing algorithms use a triangle, a ring or a circle as the geometric shape that defines the node’s residence area. However, existing algorithms suffer from two major problems: (1) in some cases, they might make wrong decisions about a node presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error and high ratio of localizable nodes. In this paper, we overcome these shortcomings by introducing a new approach for determining the node’s residence area that is geometrically shaped as a half-symmetric lens. A novel half symmetric lens based localization algorithm (HSL) is proposed. HSL yields smaller residence areas, and consequently, better location accuracy than contemporary schemes. HSL further employs Voronoi diagram in order to boost the percentage of localizable nodes. The performance of HSL is validated through mathematical analysis, extensive simulations experiments and prototype implementation. The validation results confirm that HSL achieves better location accuracy and higher ratio of localizable nodes compared to competing algorithms.
机译:基于区域的定位算法仅使用某些参考节点(称为锚点)的位置来估计其余节点的驻留区域。现有算法使用三角形,环形或圆形作为定义节点驻留区域的几何形状。但是,现有算法存在两个主要问题:(1)在某些情况下,它们可能会针对给定区域内的节点存在情况做出错误的决定,或者(2)它们需要较高的锚点密度才能实现较低的位置估计误差和较高的比率本地化节点的数量。在本文中,我们通过引入一种确定节点的驻留区域的新方法来克服这些缺点,该方法的几何形状为半对称透镜。提出了一种基于半对称透镜的新型定位算法(HSL)。与现代方案相比,HSL产生的居住区更小,因此位置精度更高。 HSL进一步采用Voronoi图以提高可本地化节点的百分比。 HSL的性能通过数学分析,广泛的模拟实验和原型实现得到了验证。验证结果证实,与竞争算法相比,HSL可以实现更好的定位精度和更高的可定位节点比例。

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