论文标题
无线传感器网络中没有传感器参数的分散源本地化
Decentralized Source Localization without Sensor Parameters in Wireless Sensor Networks
论文作者
论文摘要
本文研究了故障模型下的分散无线传感器网络（WSN）中的源（事件）本地化问题，而不知道传感器参数。事件本地化具有许多应用程序，例如本地化入侵者，WiFi热点和用户以及电源系统中的故障。先前的研究假设传感器参数的真实知识（或良好的估计）（例如，故障模型概率或源源的影响区域（ROI））用于源定位。但是，我们提出了两种方法来估计本文中的源位置下的故障模型：点击设置方法和特征选择方法，它们仅利用融合中心的嘈杂数据集来估算源位置，而不知道传感器参数。所提出的方法已显示出有效地定位源的定位。我们还研究了击中设置方法的样品复杂性要求的下限。这些方法也已扩展到多个来源的本地化。此外，我们修改了提出的特征选择方法，以使用最大可能性。最后，对不同的设置（即传感器节点的数量和样品复杂性）进行了广泛的模拟，以验证我们所提出的方法与质心，最大似然，FTML，快照估计器相比。
This paper studies the source (event) localization problem in decentralized wireless sensor networks (WSNs) under the fault model without knowing the sensor parameters. Event localizations have many applications such as localizing intruders, Wifi hotspots and users, and faults in power systems. Previous studies assume the true knowledge (or good estimates) of sensor parameters (e.g., fault model probability or Region of Influence (ROI) of the source) for source localization. However, we propose two methods to estimate the source location in this paper under the fault model: hitting set approach and feature selection method, which only utilize the noisy data set at the fusion center for estimation of the source location without knowing the sensor parameters. The proposed methods have been shown to localize the source effectively. We also study the lower bound on the sample complexity requirement for hitting set method. These methods have also been extended for multiple sources localizations. In addition, we modify the proposed feature selection approach to use maximum likelihood. Finally, extensive simulations are carried out for different settings (i.e., the number of sensor nodes and sample complexity) to validate our proposed methods in comparison to centroid, maximum likelihood, FTML, SNAP estimators.
