Smart fog computing for efficient situations management in smart health environments

• Main Authors: Achouri, Mounir, Alti, Adel, Derdour, Makhlouf, Laborie, Sébastien, Roose, Philippe
• Format: Article
• Published: Universiti Utara Malaysia 2018
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/24938/; http://jict.uum.edu.my/index.php/current-issues-1#a

Ontologies are considered a backbone for supporting advanced situation management in various smart domains, particularly smart health. It plays a vital role in understanding user context in order to determine patients’ safety, situation identification accuracy, and provide personalized comfort. The smart health domain contains a huge number of different types of context profiles related to interactive devices, linked health objects, and smart-home. The key role of context profiles is to deduce urgent situations that are needed to run adaptation components on a specific smarthealth Fog. Existing platforms and middlewares lack support to efficiently analyze a large number of heterogeneous specific profiles and continuous context changing in near real time. In this paper, we focus on data and dissemination of information from services related to the field of e-health. This paper aims to provide a new generic user situation-aware profile ontology (GUSP-Onto) for a semantic description of heterogeneous users’ profiles with efficient patients’ situation management and health multimedia information dissemination related to smart health services. Based on the users’ situation management ontology, a two-layered architecture was proposed. The first layer is used to achieve a quality diagnosis of urgent situations including a smart fog computing enhanced with semantic profile modeling that offers efficient situation management. The second layer allows a more in-depth situation analysis for patients and enhanced rich services using cloud computing that provides good scalability. The most innovative of this architecture is the potential benefits from the semantic representation to conduct emergency situation knowledge reasoning and ultimately realize early service selection and adaptation process. The experimental results show a decreased time response and an enhanced accuracy of the proposed approach.