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Network-aware positioning in sensor networks

机译:网络感知传感器网络中的定位

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Out of its importance to various applications and services, the geographical location of the sensed event is to be associated with the event itself being reported. Despite the numerous number of localization algorithms proposed, very few of them are really ad-hoc methods that are appropriate for sensor networks. In this paper, our contribution is double-folded. First, we design an experimental framework to evaluate localization methods for sensor networks. We use this framework to evaluate three localization methods: ad-hoc positioning system (APS), multi-dimensional scaling (MDS), and semi-definite programming (SDP). Using this evaluation, we identify five network properties that affect the localization accuracy. Second, we propose an adaptive localization method that we refer to as: network-aware positioning (NAP). NAP starts by assuming known network properties. Given these properties, NAP determines the best localization algorithm to use. Simulation results show that NAP performs the best among the three algorithms under all network conditions.
机译:出于各种应用和服务的重要性,所谓的事件的地理位置与报告的事件本身相关联。尽管提出了许多本地化算法,但它们中的很少是适合传感器网络的ad-hoc方法。在本文中,我们的贡献是双重折叠的。首先,我们设计一个实验框架来评估传感器网络的本地化方法。我们使用此框架来评估三种本地化方法:ad-hoc定位系统(AP),多维缩放(MDS)和半定编程(SDP)。使用此评估,我们确定了影响本地化准确性的五个网络属性。其次,我们提出了一种自适应定位方法,我们指的是:网络感知定位(NAP)。 NAP通过假设已知的网络属性启动。鉴于这些属性,NAP确定要使用的最佳本地化算法。仿真结果表明,NAP在所有网络条件下的三种算法中执行最佳。

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