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无线传感器网络的无 GPS 定位算法。

GPS-free localization algorithm for wireless sensor networks.

机构信息

School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.

出版信息

Sensors (Basel). 2010;10(6):5899-926. doi: 10.3390/s100605899. Epub 2010 Jun 9.

Abstract

Localization is one of the most fundamental problems in wireless sensor networks, since the locations of the sensor nodes are critical to both network operations and most application level tasks. A GPS-free localization scheme for wireless sensor networks is presented in this paper. First, we develop a standardized clustering-based approach for the local coordinate system formation wherein a multiplication factor is introduced to regulate the number of master and slave nodes and the degree of connectivity among master nodes. Second, using homogeneous coordinates, we derive a transformation matrix between two Cartesian coordinate systems to efficiently merge them into a global coordinate system and effectively overcome the flip ambiguity problem. The algorithm operates asynchronously without a centralized controller; and does not require that the location of the sensors be known a priori. A set of parameter-setting guidelines for the proposed algorithm is derived based on a probability model and the energy requirements are also investigated. A simulation analysis on a specific numerical example is conducted to validate the mathematical analytical results. We also compare the performance of the proposed algorithm under a variety multiplication factor, node density and node communication radius scenario. Experiments show that our algorithm outperforms existing mechanisms in terms of accuracy and convergence time.

摘要

定位是无线传感器网络中最基本的问题之一,因为传感器节点的位置对于网络操作和大多数应用层任务都至关重要。本文提出了一种无需 GPS 的无线传感器网络定位方案。首先,我们开发了一种标准化的基于聚类的本地坐标系形成方法,其中引入了一个乘法因子来调节主节点和从节点的数量以及主节点之间的连接程度。其次,使用齐次坐标,我们推导出了两个笛卡尔坐标系之间的变换矩阵,以便有效地将它们合并到全局坐标系中,并有效地克服翻转模糊问题。该算法无需集中式控制器即可异步运行,并且不需要预先知道传感器的位置。基于概率模型推导出了一组用于该算法的参数设置准则,并研究了能量需求。通过具体的数值示例进行了仿真分析,验证了数学分析结果。我们还比较了在不同乘法因子、节点密度和节点通信半径场景下提出的算法的性能。实验表明,我们的算法在准确性和收敛时间方面优于现有机制。

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