ANovelManifoldLearningAlgorithmforLocalizationEst.pptVIP

A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks Shancang LI,Student Member and Deyun ZHANG,Nonmember IEICE TRANS.COMMUN.,VOL.E90–B,NO.12 DECEMBER 2007 SUMMARY We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods. Key words: wireless sensor network, localization, manifold algorithm 我们提出一种精确的分布定位算法，算法利用连接测量来定位节点。算法基于自组织等距嵌入算法，适应于着重最精确的测量范围和解决了传感器网络中的通信限制。每个节点选择了一个相邻的传感器并且通过最小化本地消耗功能更新其位置估计，然后把更新传到相邻传感器。仿真证明算法比以前的方法对测量误差更具鲁棒性，它能完成更好的结果且使用更少的固定节点。 Introduction 大多数的应用要求知道节点的位置或至少它们的相关位置。由于网络由很多节点组成，不可能每个节点的位置都预先确定。一个方法可以消除额外的定位硬件需求从而扩展了WSNs的应用范围。本文提出计算节点位置的一种方法，方法只需要有描述节点通信范围内有哪些节点的基本连接信息。该方法称之为SIEMAP，有三个步骤。首先，要求有无线传感器网络的连接信息，一个all-pairs最短路径算法用于粗略估计每对可能的节点之间的距离。然后源自数学心理学的多维测量MDS技术用于节点定位。最后我们规范结果坐标以考虑任何已知位置的节点。 First, the connectivity information of wireless sensor networks is required, and an all-pairs shortest paths algorithm is used to roughly estimate the distances between each possible pair of nodes. Then a multidimensional scaling(MDS) technique from mathematical psychology is used to derive node locations that fit these estimated distances. Finally, we normalize the resulting coordinates to take into account any nodes whose positions are known. 我们将证明SIEMAP优于目前方法。它只需连接信息以确定精确位置。如果可以获得相邻节点间的距离可以获得，在初始阶段，它们就能很容易的合并为成对的最短路径计算。SIEMAP建立的坐标最合适