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Localization with Ratio-Distance (LRD) for Distributed and Accurate Localization in Wireless Sensor Networks

机译:用于无线传感器网络中的分布式和精确定位的比率距离定位(LRD)定位

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These days, cheap and intelligent sensors, networked through wireless links and deployed in large numbers, provide unprecedented opportunities for monitoring and controlling homes, cities and the environment. Networked sensors also offer a broad range of applications. Localization capability is essential in most wireless sensor networks applications; for instance in environmental monitoring applications such as animal habitat monitoring, bush fire surveillance, water quality monitoring and precision agriculture, the measurement data are meaningless without accurate knowledge of where they are obtained. Localization techniques are used to determine location information by estimating the location of each sensor node. Distance measurement errors are commonly known to affect the accuracy of the estimated location; resulting in errors that may be due to inherent or environmental factors. Trilateration [1] is a well-known method for localizing nodes by using the distances to three anchor nodes; yet it performs poorly when they are many distance measurement errors. Therefore, we propose the LRD (Localization with Ratio-Distance) algorithm, which performs strongly even in the presence of many measurement errors associated with the estimated distance to anchor nodes. Simulations using the OPNET Modeler show that LRD is more accurate than trilateration.
机译:如今,廉价且智能的传感器通过无线链接联网并大量部署,为监视和控制房屋,城市和环境提供了前所未有的机会。网络传感器也提供了广泛的应用。本地化功能在大多数无线传感器网络应用中至关重要。例如,在诸如动物栖息地监视,丛林火灾监视,水质监视和精确农业之类的环境监视应用中,如果不准确知道它们的获取位置,那么测量数据就毫无意义。定位技术用于通过估计每个传感器节点的位置来确定位置信息。众所周知,距离测量误差会影响估计位置的准确性;导致可能是由于固有或环境因素引起的错误。 Trilateration [1]是一种众所周知的方法,它通过使用到三个锚节点的距离来定位节点。但是,当它们有很多距离测量错误时,它的性能就会很差。因此,我们提出了LRD(比例距离本地化)算法,该算法即使在存在许多与到锚节点的估计距离相关的测量误差的情况下也能表现出色。使用OPNET Modeler进行的仿真表明,LRD比三边测量更准确。

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