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.

中文翻译：

---

物联网系统中高斯过程回归的数据辅助传感

在本文中，为了在有限的带宽内进行有效的数据收集，将数据辅助感测应用于高斯过程回归，该过程用于学习从物联网系统中的传感器收集的数据集。我们将重点放在传感器测量的内插值上，这些数据是使用高斯过程回归和数据辅助传感从一部分传感器上传的少量测量值中得出的。归功于主动传感器的选择，与随机选择相比，具有数据辅助感测的高斯过程回归可以提供完整数据集的良好估计。使用多通道ALOHA，当传感器可以收到其测量预测的反馈信息时，数据辅助感测通常用于分布式选择性上载，以便每个传感器可以通过将其测量结果与预测的测量值进行比较来决定是否上载。数值结果表明，与具有相同上载概率的传统多通道ALOHA相比，带有预测的改进多通道ALOHA可以帮助提高具有数据辅助感测的高斯过程回归的性能。