In order to estimate the location of the user, the previous studies introduced many sensor deployment methods applying the sensor network. The important issues to consider when placing the sensors are a configuration cost and detection area of a sensor network. In other words, the sensors consisting the network should be optimally deployed by taking into account the coverage and connectivity of them. In this paper, a sensor signal is modeled as the Gaussian distribution based signal points group, and signal points in overlapping region between two different sensors are classified by e-SVM (support vector machine) method as each sensor group. In addition, a trilateration technique is used for estimating the position of a moving object. At this time, we efficiently deploy the sensors with f-Apriori method to maintain the connectivity between the sensors as well as to apply the trilateration. The proposed method can be utilized for optimal placement of sensors if we know a detection range of one sensor. In this paper, we introduce more effective and adaptive deployment method to consist the sensor network as taking into account the cost and the situation.

中文翻译：

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SensDeploy：用于实时本地化的高效传感器部署策略

为了估计用户的位置，先前的研究介绍了许多应用传感器网络的传感器部署方法。放置传感器时要考虑的重要问题是传感器网络的配置成本和检测范围。换句话说，组成网络的传感器应该通过考虑它们的覆盖范围和连通性来进行最佳部署。在本文中，将传感器信号建模为基于高斯分布的信号点组，并将两个不同传感器之间重叠区域中的信号点通过e -SVM（支持向量机）方法分类为每个传感器组。另外，三边测量技术用于估计移动物体的位置。在这个时候，我们有效地部署与传感器˚F-Apriori方法用于保持传感器之间的连接以及应用三边测量。如果我们知道一个传感器的检测范围，则可以将所提出的方法用于传感器的最佳放置。在本文中，考虑到成本和情况，我们引入了更有效和自适应的方法来构成传感器网络。