Distributed and in-Situ Machine Learning for Smart-Homes and Buildings: Application to Alarm Sounds Detection
Amaury Durand, Yanik Ngoko, Christophe Cérin · 2017
We consider the implementation of an in-situ machine learning system with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new orchestration system for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.