Smart Environmental Architecture for Node Localization in a Wireless Sensor Network

S. Jauregui, Mario Siller, Felix Ramos, Edson Escalabrin
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引用次数: 3

Abstract

As far as we know, there is not a Node Localization Algorithm (NLA) that presents the same accuracy for all possible scenarios. We believe that a NLA should be able to "interpret" the dynamic information of the environment. In this sense, simple NLAs are rather focused and might perform well for specific scenarios and applications. Therefore, information fusion and context awareness seems to be an appropriate approach to address this issue. We propose the Smart Environmental Architecture for Node Localization (SEA-NL), which is composed by two main elements: (i) the Smart Beacon Nodes (SBNs) and (ii) the Logical Position of Nodes (LPN). In (i) the obstruction level indicator is estimated and can improve the estimation of distances among nodes. In (ii) environment information and a one to one relation between a node and an object are used and can also improve location estimation. Via simulation, our architecture was tested indoors and outdoors considering three localization algorithms: the Weighed Centroid Localization (WCL), the Centroid Localization, and the Triangular Centroid Localization. Finally, we present an accuracy comparison among NLAs used in isolated way, and by using the SBNs, the LPN, and the SEA-NL, where our architecture improves WCL up to ~30.88%.
面向无线传感器网络节点定位的智能环境架构
据我们所知,没有一种节点定位算法(NLA)可以为所有可能的场景提供相同的精度。我们认为NLA应该能够“解释”环境的动态信息。从这个意义上说,简单的nla是相当集中的,并且可能在特定的场景和应用程序中表现良好。因此,信息融合和上下文感知似乎是解决这个问题的合适方法。我们提出了节点定位的智能环境架构(SEA-NL),它由两个主要元素组成:(i)智能信标节点(sbn)和(ii)节点的逻辑位置(LPN)。在(i)中,对障碍物等级指标进行估计,可以改进节点间距离的估计。在(ii)使用环境信息和节点与对象之间的一对一关系,也可以改进位置估计。通过模拟,我们的架构在室内和室外测试了三种定位算法:加权质心定位(WCL),质心定位和三角质心定位。最后,我们对单独使用的nla进行了精度比较,并使用了sbn、LPN和SEA-NL,其中我们的架构将WCL提高了约30.88%。
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