Virtual Things for Machine Learning Applications
Bovet , Gérôme
Ridi , Antonio
Hennebert , Jean
- Publisher: HAL CCSD
[ INFO.INFO-IU ] Computer Science [cs]/Ubiquitous Computing | Sensor network | Machine learning | Web-of-Things
International audience; Internet-of-Things (IoT) devices, especially sensors are pro-ducing large quantities of data that can be used for gather-ing knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture able to ex-ecute machine learning algorithms within the sensor net-work, presenting advantages in terms of privacy and data transfer efficiency. We first argument that some classes of machine learning algorithms are compatible with this ap-proach, namely based on the use of generative models that allow a distribution of the computation on a set of nodes. We then detail our architecture proposal, leveraging on the use of Web-of-Things technologies to ease integration into networks. The convergence of machine learning generative models and Web-of-Things paradigms leads us to the con-cept of virtual things exposing higher level knowledge by exploiting sensor data in the network. Finally, we demon-strate with a real scenario the feasibility and performances of our proposal.