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A Link Quality Estimation Method for Wireless Sensor Networks Based on Deep Forest

机译：基于深林的无线传感器网络的链路质量估计方法

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摘要

In wireless sensor networks, sensor nodes, the miniature embedded devices, have limitation of energy, storage, computing, and etc. One of the tasks of the nodes is to use their limited resources to complete work efficiently. Choosing high quality link communication can effectively save energy. In this paper, we propose a link quality estimation model that is based on deep forest. To avoid a noise sample becoming a center point in the clustering, we use an improved K-medoids algorithm based on step increasing and optimizing medoids (INCK) when dividing the link quality grades. During the sample preprocessing stage, the Pauta criterion is used to delete the noise link samples, and we fill the mean value of each grade into the missing values. The feature extraction performance of deep forest is improved by combining the stratified sampling to change the unbalance distribution of link quality samples. And then the Stratified Sampling Cascade Forest link quality estimation (SCForest-LQE) is constructed by combining stratified sampling with cascade forest. The experiments are conducted in three real application scenarios. Compared with the existing six link quality estimation models, SCForest-LQE has better estimation performance and stability.
机译：在无线传感器网络中，传感器节点，微型嵌入式设备，具有能量，存储，计算等的限制。节点的任务之一是使用它们有限的资源有效地完成工作。选择高质量的链接通信可以有效节省能源。在本文中，我们提出了一种基于深林的链路质量估计模型。为避免噪声样本成为聚类中的中心点，我们使用基于逐步增加和优化瞳孔（基于链路质量等级的METOIDS（INCK）的改进的K-METOIDS算法。在样本预处理阶段，Pauta标准用于删除噪声链路样本，并且我们将每个等级的平均值填充到缺失值中。通过组合分层采样改变链路质量样本的不平衡分布，改善了深森林的特征提取性能。然后通过将分层采样与梯级林相结合来构建分层采样级级森林链路质量估计（Sc Forest-LQE）。实验是在三种实际应用方案中进行的。与现有的六个链路质量估计模型相比，ScureSt-LQE具有更好的估计性能和稳定性。

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