Appliance Recognition on Internet-of-Things Devices

Gérôme Bovet (1) , Antonio Ridi (2) , Jean Hennebert (2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 UNIFR - Université de Fribourg = University of Fribourg

Machine Learning (ML) approaches are increasingly used to model data coming from sensor networks. Typical ML implementations are cpu intensive and are often running server-side. However, IoT devices provide increasing cpu capabilities and some classes of ML algorithms are compatible with distribution and downward scalability. In this demonstration we explore the possibility of distributing ML tasks to IoT devices in the sensor network. We demonstrate a concrete scenario of appliance recognition where a smart plug provides electrical measures that are distributed to WiFi nodes running the ML algorithms. Each node estimates class-conditional probabilities that are then merged for recognizing the appliance category. Finally, our architectures relies on Web technologies for complying with Web-of-Things paradigms.

Domaines: Informatique ubiquitaire

Gérôme Bovet, Antonio Ridi, Jean Hennebert. Appliance Recognition on Internet-of-Things Devices. Internet of Things 2014, Oct 2014, Boston, United States. ⟨hal-01118962⟩