Ontology based context aware situation tracking

Abstract

Ubiquitous intelligent devices have enabled provision of smart services to people in seamless way. Context-awareness helps understand current state-of-affairs or the situation in which presently the system is. This understanding helps the IoT application provide more relevant and smarter services based on situations that change over a period of time. In this paper, we propose a novel context-aware situation-tracking framework that makes use of an ontology. The ontology represents the conceptual model of a dynamic world, where situations evolve over time in changing contexts. The ontology provides the reasoning framework to infer about a situation based on the input context data as well as the past information of earlier situations. Future situations can be predicted with some belief based on current situation and incoming context data. The context data is acquired from sensor devices and external inputs. For every recognized situation, system recommends some actions to provide context-aware service. We use Multimedia Web Ontology Language (MOWL) to represents the ontology. MOWL proposes a probabilistic framework for reasoning with uncertainties linked with observation of context. It makes use of Dynamic Bayesian networks to predict and track the dynamically changing situations. We illustrate use of this framework for Smart Mirror use case.