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The term pervasive or ubiquitous introduced first by Weiser (1991) refers to the seamless integration of devices into the users everyday life. Weiser defined pervasive systems as those that weave themselves into the fabric of everyday life until they are indistinguishable from it. To make this vision a reality, systems should vanish into the background to make the user and his actions the central focus rather than computing devices and technical issues.
In the recent years, we have witnessed rapid advances in mobile technologies, software paradigms, Internet of Things (IoT) technologies (Belkeziz, 2020) (Kinnunen, 2018), Human Behavior Analysis (Kaur & Kautish, 2019), embedded sensor networks and wide range of wired and wireless protocols. Nowadays, a number of leading technological organizations are exploring pervasive computing; however, the solutions developed up to now are far from becoming reality the Weiser's vision. A step further towards this vision is the research in context-aware services oriented systems development. These systems are able to adapt to the current context, making that user intervention is sought only when it is absolutely required. Thus, these systems aim at increasing usability and effectiveness by taking context into account.
To address the service adaptation issue, it is important to have an efficient mechanism to capture and manage context, an appropriate reasoning engine and also a dedicated tool to adapt services according to user’s context.
Several approaches have been proposed to deal with Context Aware Services (CAS), but most, only offer weak support for knowledge sharing and context reasoning. Ontology based approaches presents the most promising instrument for context modeling and offers possibility of applying ontology reasoning techniques. Modeling context using an ontology-based approach allows to describe contexts semantically in a way which is independent of programming language, underlying operating system or middleware. The main benefit of this model is that it enables formal analysis of domain knowledge. It also enables sharing common understanding of the structure of context information among users, devices and services to enable semantic interoperability.
In addition, context-aware systems, as well as having a suitable context model, they must first have sufficient intelligence and knowledge-awareness to react appropriately according to context, to do this it is necessary to reason about context at semantic level to interpret this information and obtain knowledge from it. According to this knowledge, the system must adapt its behaviour to meet the needs of users within ever-changing context.
For the above reasons, CAS development can benefit from Ontologies, Model Driven Engineering (MDE) via ODM (OMG, 2014) and Semantic Web Services. Ontologies allow the representation of complex context. Model-Driven Engineering is a software design and development approach that strongly focuses on models and pretends that these models can be refined and finally transformed into a technical implementation. Semantic Web Services (Klusch, 2016), as a new research paradigm, is generally defined as the augmentation of Web Service descriptions through Semantic Web annotations. In this work, we aim to propose in first stage an overview of an architecture for the development of context aware services based on ontologies, we present our ontology based context metamodel and in the second stage we discuss about context reasoning, at the end we present our contribution in semantic context-aware service by extending OWL-S (Martin, 2004) and especially, we present our Service Selection and Adaptation Tool.