RSD: A Metric for Achieving Range-Free Localization beyond Connectivity
RSD: A Metric for Achieving Range-Free Localization beyond Connectivity
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DOI：
10.1109/tpds.2011.105
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发表时间：
2011-11
影响因子：
5.3
通讯作者：
Ziguo Zhong;T. He
中科院分区：
文献类型：
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作者：
Ziguo Zhong;T. He
无线传感器网络被认为是许多位置相关应用的有前途的工具。在此类部署中，低系统成本的要求阻碍了许多基于范围的传感器节点定位方法；另一方面，仅依赖于无线电连接的无距离方法可能无法充分利用邻近感测中嵌入的邻近信息。针对这些限制，本文引入了一种称为 RSD 的邻近度度量，以无范围的方式捕获 1 跳相邻节点之间的距离关系。 RSD 的开销很小，可以方便地用作最先进的基于连接的定位解决方案的透明支撑层，以实现更高的精度。我们使用三种著名的算法实现了 RSD，并使用两个室外测试台进行了评估：一个带有 54 个 MICAz 微尘的 850 英尺长的线性网络，以及一个覆盖 10,000 平方英尺的带有 49 个微尘的常规 2D 网络。结果表明，我们的设计有助于通过子跳分辨率消除估计模糊性，并将定位错误减少多达 35%。此外，仿真证实了其对于大规模网络的有效性，并揭示了在不均匀分布的无线电路径损耗下的鲁棒性的有趣特征。
Wireless sensor networks have been considered as a promising tool for many location-dependent applications. In such deployments, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free approaches depending only on radio connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to these limitations, this paper introduces a proximity metric called RSD to capture the distance relationships among 1-hop neighboring nodes in a range-free manner. With little overhead, RSD can be conveniently applied as a transparent supporting layer for state-of-the-art connectivity-based localization solutions to achieve better accuracy. We implemented RSD with three well-known algorithms and evaluated using two outdoor test beds: an 850-foot-long linear network with 54 MICAz motes, and a regular 2D network covering an area of 10,000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with a subhop resolution, and reduces localization errors by as much as 35 percent. In addition, simulations confirm its effectiveness for large-scale networks and reveal an interesting feature of robustness under unevenly distributed radio path loss.