Data-Aided Sensing for Gaussian Process Regression in IoT Systems

In this article, for efficient data collection with limited bandwidth, data-aided sensing (DAS) is applied to the Gaussian process regression (GPR) that is used to learn data sets collected from sensors in the 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 GPR with DAS. Thanks to active sensor selection, it is shown that GPR with DAS can provide a good estimate of a complete data set compared to that with random selection. With multichannel ALOHA, DAS 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 GPR with DAS compared to the conventional multichannel ALOHA with equal uploading probability.