Currently, the field of smart-* (home, city, health, tourism, etc.) is naturally heterogeneous and multimedia oriented. In such a domain, there is an increasing usage of heterogeneous mobile devices, as well as captors transmitting data (IoT). They are highly connected and can be used for many different services, such as to monitor, to analyze and to display information to users. In this context, data management and adaptation in real time are becoming a challenging task. More precisely, at one time, it is necessary to handle in a dynamic, intelligent and transparent framework various data provided by multiple devices with several modalities. This paper presents a Kali-Smart platform, which is an autonomic semantic-based context-aware platform. It is based on semantic web technologies and a middleware providing autonomy and reasoning facilities. Moreover, Kali-Smart is generic and, as a consequence, offers to users a flexible infrastructure where they can easily control various interaction modalities of their own situations. An experimental study has been made to evaluate the performance and feasibility of the proposed platform.

This paper presents a software platform (called Kalimucho) for mobile and embedded hardware. The architecture of such application is based on two component models: Osagaia for software containers and Korrontea for connector containers. Kalimucho uses service factory permitting the creation of instances of software components. This factory allows making the most suitable configuration according to services decision managing reconfiguration. The reconfiguration service decisions depend on data sent by control interfaces and the users defined rules (via the platform and control interfaces).

Purpose – The purpose of this paper is to present a supervised adaptation platform for applications-based components. Design/methodology/approach – The platform is designed using a model based top-down approach. The authors use UML diagrams and particularly scenarios and activity diagrams. Findings – The CSC (component, service and connector) platform is based on a component/service model that allows adaptation of component-based applications and uses service-oriented architecture for providing adaptation services to be embedded in adaptation connectors. Originality/value – The paper proposes CSC, a self-adaptation platform based on MMSA, to describe software architectures for multimedia-oriented application and providing adaptation capabilities. The platform is based on services and offer architecture, with three layers particularly adapted to adaptation of multimedia flow (types, formats, properties) and which allows solving the heterogeneity problems of components.

Currently, multimedia contents can be accessed at anytime and anywhere with a wide variety of devices, such as laptops, smartphones and tablets. In this paper, we present a service-based multimedia adaptation architecture where adaptation services can be available locally and remotely on multiple platforms. When conflicts are detected between some multimedia document properties and profile constraints, we propose a procedure that selects relevant adaptation services in order to compute an adapted document. This procedure may be executedlocally or remotely, moreover for unsovled conflicts this one indicates which services may have to be installed

Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets.