论文标题
物联网系统中高斯过程回归的数据辅助感应
Data-aided Sensing for Gaussian Process Regression in IoT Systems
论文作者
论文摘要
在本文中，对于具有有限带宽的有效数据收集，将数据辅助传感应用于高斯流程回归，该过程用于学习从贸易Internet Systems中的传感器收集的数据集。我们专注于使用与数据辅助传感的高斯过程回归的一小部分传感器上载传感器测量的测量值。得益于主动传感器的选择，可以证明具有数据辅助传感的高斯过程回归可以提供与随机选择相比的完整数据集的良好估计。使用多通道Aloha，当传感器可以反馈其测量值的预测时，数据辅助传感被推广用于分布式选择性上传，以便每个传感器可以通过将其测量值与预测的测量方法进行比较来决定上传。数值结果表明，与常规的多通道Aloha相比，具有相等的上传概率的经过预测的修改多通道Aloha可以有助于通过数据辅助传感提高高斯过程回归的性能。
In this paper, for efficient data collection with limited bandwidth, data-aided sensing is applied to Gaussian process regression that is used to learn data sets collected from sensors in Internet-of-Things systems. We focus on the interpolation of sensors' measurements from a small number of measurements uploaded by a fraction of sensors using Gaussian process regression with data-aided sensing. Thanks to active sensor selection, it is shown that Gaussian process regression with data-aided sensing can provide a good estimate of a complete data set compared to that with random selection. With multichannel ALOHA, data-aided sensing is generalized for distributed selective uploading when sensors can have feedback of predictions of their measurements so that each sensor can decide whether or not it uploads by comparing its measurement with the predicted one. Numerical results show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing compared to conventional multichannel ALOHA with equal uploading probability.
