Gaussian process regression is used to learn data sets collected from sensors in Internet-of-Things systems . Data-aided sensing is generalized for distributedselective 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 . The results show that modified multichannel ALOHA with predictions can help improve the performance of Gauss process regression with data-Aided sensing compared to conventionalmultichannelALOHA . With predictions, the results can also be improved with equal uploading probability with equal upload probability . In this paper, we focus on the interpolation of sensors’ measurements from a small number ofmeasurements uploaded

Author(s) : Jinho Choi

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Keywords : data - process - regression - predictions - sensors -

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