Localization for anisotropic sensor networks
Localization for anisotropic sensor networks
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DOI：
10.1109/infcom.2005.1497886
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发表时间：
2005-03
期刊：
影响因子：
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通讯作者：
Hyuk Lim;J. Hou
中科院分区：
文献类型：
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作者：
Hyuk Lim;J. Hou
在本文中，我们考虑各向异性传感器网络的定位问题。各向异性网络与各向同性网络的区别在于，它们具有根据测量方向而变化的属性。各向异性特性由各种因素引起，例如区域的地理形状（非凸区域）、不同的节点密度、不规则的无线电模式以及各向异性的地形条件。为了表征各向异性特征，我们设计了一种线性映射方法，通过使用截断奇异值分解（TSVD为基础的）伪逆技术将传感器节点之间的邻近测量转换到地理距离嵌入空间。这种变换保留了尽可能多的拓扑信息，并减少了测量噪声对地理距离估计的影响。仿真结果表明，该定位方法优于DV-hop、DV-distance（D。Niculescu，2001）和MDS-图（Y. Shang等人，2003），并在各向同性和各向异性传感器网络中对传感器位置进行鲁棒和准确的估计。
In this paper, we consider the issue of localization in anisotropic sensor networks. Anisotropic networks are differentiated from isotropic networks in that they possess properties that vary according to the direction of measurement. Anisotropic characteristics result from various factors such as the geographic shape of the region (non-convex region), the different node densities, the irregular radio patterns, and the anisotropic terrain conditions. In order to characterize anisotropic features, we devise a linear mapping method that transforms proximity measurements between sensor nodes into a geographic distance embedding space by using the truncated singular value decomposition-based (TSVD-based) pseudo-inverse technique. This transformation retains as much topological information as possible and reduces the effect of measurement noises on the estimates of geographic distances. We show via simulation that the proposed localization method outperforms DV-hop, DV-distance (D. Niculescu, 2001), and MDS-map (Y. Shang et al., 2003), and makes robust and accurate estimates of sensor locations in both isotropic and anisotropic sensor networks.