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GPS-less low-cost outdoor localization for very small devices

机译:无需GPS的低成本户外定位功能,可用于非常小的设备

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摘要

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.
机译:通过大型无线传感器节点网络对物理世界进行仪表化,尤其是对于诸如水和土壤环境监测之类的应用程序,要求这些节点非常小,轻巧,不受束缚且不引人注目。定位问题,即确定给定节点在网络中的物理位置,是一个具有挑战性的问题,但对于许多这些应用程序来说却极为重要。诸如节点的小尺寸,形状因数,成本和功率约束之类的实际考虑因素排除了这些网络中所有节点对GPS的依赖。我们回顾了定位技术,并评估了一种非常简单的连通性度量方法在室外环境中进行定位的有效性,该方法利用了这些设备的固有RF通信功能。网络中具有覆盖范围重叠区域的固定数量的参考点将发送周期性的信标信号。节点使用一种简单的连接性度量标准来推断与这些参考点的给定子集的接近程度,该度量标准对于环境变化更为稳健。节点将自身定位到其邻近参考点的质心。然后,定位的精度取决于两个相邻参考点之间的间隔距离以及这些参考点的传输范围。初步的实验结果表明,我们90%的数据点的准确度都在分隔距离的三分之一以内。但是,需要将来的工作才能将该技术扩展到更杂乱的环境。

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