arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1502.07790cs.NIcs.CG
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利用共面簇增强无线传感器网络的三维定位

Exploiting Coplanar Clusters to Enhance 3D Localization in Wireless Sensor Networks

Onur Cagirici

摘要:

本文研究了在三维共面环境下基于距离的无线传感器网络定位问题。在大多数实际应用中,即使环境是3D的,接地传感器节点通常部署在2D平面上。这些表面的例子包括在室内和室外都能看到的结构(例如地板、门、墙壁、桌子等)。和室外(例如,山脉、山谷、丘陵等)环境.在这样的环境中,传感器节点通常表现为共面节点簇。我们将这种类型的展开称为平面展开。当存在平面部署时,共面性给传统的基于距离的定位算法带来困难,因为如果到该节点的距离测量来自共面节点,则该节点不能被明确地定位。因此,许多已经局部化的节点组在该过程中变得无效,仅仅因为它们是共面的。因此,我们提出了一种算法,称为共面性基于定位(CBL),可以用作任何定位算法的扩展,以避免大多数翻转所造成的共面性。CBL首先在聚集在同一表面上的节点之间执行2D定位,然后在3D中找到这些簇的位置。我们已经进行了实验,使用三边测量的2D本地化,和四边形的3D本地化,并通过实验验证,利用聚类信息导致一个更精确的定位比单纯的四边形。我们还提出了一个启发式提取聚类信息的情况下,它是不可用的,这是有待于在未来改进。

英文摘要:

This thesis studies range-based WSN localization problem in 3D environments that induce coplanarity. In most real-world applications, even though the environment is 3D, the grounded sensor nodes are usually deployed on 2D planar surfaces. Examples of these surfaces include structures seen in both indoor (e.g. floors, doors, walls, tables etc.) and outdoor (e.g. mountains, valleys, hills etc.) environments. In such environments, sensor nodes typically appear as coplanar node clusters. We refer to this type of a deployment as a planar deployment. When there is a planar deployment, the coplanarity causes difficulties to the traditional range-based multilateration algorithms because a node cannot be unambiguously localized if the distance measurements to that node are from coplanar nodes. Thus, many already localized groups of nodes are rendered ineffective in the process just because they are coplanar. We, therefore propose an algorithm called Coplanarity Based Localization (CBL) that can be used as an extension of any localization algorithm to avoid most flips caused by coplanarity. CBL first performs a 2D localization among the nodes that are clustered on the same surface, and then finds the positions of these clusters in 3D. We have carried out experiments using trilateration for 2D localization, and quadrilateration for 3D localization, and experimentally verified that exploiting the clustering information leads to a more precise localization than mere quadrilateration. We also propose a heuristic to extract the clustering information in case it is not available, which is yet to be improved in the future.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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