Cooperative Localization with Pre-Knowledge Using Bayesian Network for Wireless Sensor Networks

Shih-Hsiang Lo, Chun-Hsien Wu, Yeh-Ching Chung
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引用次数: 6

Abstract

Obtaining location information by localization schemes for sensor nodes makes applications of wireless sensor networks (WSNs) more meaningful. Most of localization schemes only use the information gathered during the execution of the localization scheme. In this paper, we proposed a location model based on Bayesian Network [18] with proximity measurement, the deployment information, and the deployment knowledge to describe the relations of the locations of sensor nodes deployed in a grid topology with the probabilistic graphical model. Based on the location model, we present a cooperative localization algorithm, the CLPKBN scheme, to do the localization for a WSN. To evaluate the proposed scheme, we implement the CLPKBN scheme and the Probability Grid scheme on a simulator. The experimental results show that the CLPKBN scheme outperforms the Probability Grid scheme in most of test cases.
基于贝叶斯网络的无线传感器网络协同定位
通过定位方案获取传感器节点的位置信息,使无线传感器网络的应用更有意义。大多数定位方案只使用定位方案执行过程中收集到的信息。本文提出了一种基于贝叶斯网络[18]的位置模型,结合接近度测量、部署信息和部署知识,用概率图模型来描述部署在网格拓扑中的传感器节点的位置关系。在定位模型的基础上,提出了一种基于CLPKBN的协同定位算法来实现无线传感器网络的定位。为了评估所提出的方案,我们在模拟器上实现了CLPKBN方案和概率网格方案。实验结果表明,在大多数测试用例中,CLPKBN方案优于概率网格方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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