Situation reasoning framework for the Internet of Things environments using deep learning results
Seyoung Park, Mye M. Sohn, Haeran Jin, Hyunjung Lee · 2016
A goal of this paper is to suggest a framework to infer the situation using IOT sensor data. To do so, the framework adopts contexts which were derived from the learning results of multiple deep neural networks for IOT sensor data and carries out hierarchical clustering of contexts in terms of the spatio-temporality. With the learned dendrogram, the most appropriate situation is inferred from case-based reasoning depending on the similar time and location. The result of reasoning is stored in a case memory and this can contribute to learning of a case base. The primary contribution of this paper is the situation reasoning under consideration for spatio-temporality that is a characteristic of IOT sensor data. Also, we performed experiments to show the superiority of our framework. The experimental results are not bad for a first attempt. In further research, if the algorithms are improved, we can expect better results.