Migrating software to mobile technology: a model driven engineering approach

Nowadays, organizations are facing the problematic of having to modernize or replace their legacy software. This software has involved the investment of money, time and other resources through the ages and there is a high risk in replacing it. The purpose of reengineering is to adapt software in a disciplined way in order to improve its quality in aspects such as operability, functionality or performance. The focus of reengineering is on improving an existing system with a higher return on investment than would be achieved by developing a new system. In the context of reengineering, the term legacy was associated with software that survived several generations of developers, administrators and users. The entry into the market of new technologies or paradigms is increasingly occurring and, motivates the growing demand for the adaptation of systems developed more recently. Mobile Computing is crucial to harvesting the potential of these new paradigms. Smartphones are the most used computing platform worldwide. They come with a variety of sensors (GPS, accelerometer, digital compass, microphone and camera) enabling a wide range of applications in Pervasive Computing, Cloud Computing and Internet of Things (IoT)

Model-Driven Methodology for Rapid Deployment of Smart Spaces based on Resource-Oriented Architectures

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym

Applying Software Product Lines to Build Autonomic Pervasive Systems

In this Master Thesis, we have proposed a model-driven Software Product Line (SPL) for developing autonomic pervasive systems. The work focusses on reusing the Variability knowledge from the SPL design to the SPL products. This Variability knowledge enables SPL products to deal with adaptation scenarios (evolution and involution) in an autonomic way.Cetina Englada, C. (2008). Applying Software Product Lines to Build Autonomic Pervasive Systems. http://hdl.handle.net/10251/12447Archivo delegad

MLContext: A Context-Modeling Language for Context-Aware Systems

Context awareness refers to systems that can both sense and react based on their environment. The complexity of these systems makes necessary to apply software engineering techniques in their development, such as Model-Driven Software development (MDD). One of the main difficulties that developers of context-aware systems must tackle is how to manage the needed context information. In this paper, we present MLContext, a textual Domain Specific Language (DSL) which is specially tailored for modeling context information and automatically generating software artefacts from context models. It has been designed to provide a high-level abstraction, to be an easy to learn, and to promote reuse of context models. We have built a toolkit including an editor and a parser to convert MLContext textual specifications into models. As a proof of concept, we have automatically generated ontologies and Java code for the OCP middleware. MLContext models can be reused in applications with the same context because they do not include details related to the platforms or the implementation. These context models can be specified by non-developers users because MLContext provides high-level abstractions of the domain

Design process enabling adaptation in pervasive heterogeneous contexts

International audienceIn the next decades, the growth in population ageing will cause important problems to most industrialized countries. To tackle this issue, Ambient Assistive Living (AAL) systems can reinforce the well-being of elderly people, by providing emergency, autonomy enhancement, and comfort services. These services will postpone the need of a medicalized environment, and will allow the elderly to stay longer at home. However, each elderly has specific needs and a deployment environment of such services is likely unique. Furthermore, the needs evolve over time, and so does the deployment environment of the system. In this paper, we propose the use of a model-based development method, the adaptive medium approach, to enable dynamic adaptation of AAL systems. We also propose improvements to make it more suited to the AAL domain, such as considering heterogeneity and a composition model. The paper includes an evaluation of the prototype implementing the approach, and a comparison with related work