Adaptive modeling and sampling methodologies for Internet of Things applications
Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver, Piet M. A. Demeester, Tom Dhaene · 2016
The past few years several cloud services offer automated machine learning software. This enables non-experts to build sophisticated predictive models so they can focus on their area of expertise instead, and use these state-of-the-art machine learning techniques. These were the same principles that guided the development of the surrogate modeling (SUMO) toolbox to assist engineers during (virtual) product design and rapid prototyping with state-of-the-art machine learning methods. A proof of concept was developed, which exposes the technologies of the SUMO toolbox as a network service, offering them to the devices attached to the same network. Both the implementation of the service as well as the possibilities for Internet of Things are discussed.