Public health organizations play a crucial role in disease prevention, health promotion, and health education. However, they face significant challenges due to fragmented data sources, delayed data collection, and inadequate analytical tools. This paper proposes a comprehensive, data-driven socio-economic model that empowers public health agencies with a global overview of disease spread and community health trends. By deploying advanced AI-driven models, the proposed solution enables predictive analytics, uncovering patterns and risk factors to forecast disease outbreaks and prioritize health interventions effectively. The system was deployed in five pilot sites (Barcelona, Birmingham, New York, Paris, and Singapore) focusing on asthma as a case study, collecting health and environmental data from 1,832 participants. Through this deployment, we demonstrated the potential for real-time data collection, AI insights and predictive analytics to enhance health outcomes and decision-making.

An Ant Colony Optimization (ACO) based approach enhances trip planning by dynamically selecting routes that are adapted to end users. As the global population ages, older adults face various physical, sensory, and cognitive challenges that affect their mobility. In our research we adopted ACO tailored for age-friendly trip planning using the Isula framework. Our proposed method focuses on optimizing trip routes by considering older adult related factors such as distance and slope, which are critical for this population. The method was applied in the first stage to the Lennoxville district of Sherbrooke, Quebec, with promising results.

A Rapid deployment solution, which enables to discover surrounding context and construct the semantic knowledge, is a major enabler for large scale deployment of Ambient Assisted Living (AAL)1 solutions in aging people environments. Therefore, we built UbiSMART framework, an AAL ontology/web solution based on a shared cloud-based semantic reasoner, to enable easy and quick deployment of hardware and software component. UbiSMART uses two ontologies: 1) a generic ontology (abstract model) that contains the shared properties and 2) a specific ontology (instance model) that contains the details of each end-user environment. UbiSMART is complemented by a simple and user-friendly services to populate and modify the instance model. This solution does not need any prior knowledge of the system or technical expertise. In this paper, we present the description of our approach, the technical design, and its implementation. We also detail the validation in real scenarios and the obtained results, as well as the lessons learned and the future perspectives.

Context-aware software provides adapted services to users or other software components. On the other hand, Autonomic Pervasive Computing uses Context to reduce the complexity of pervasive system utilization, management and maintenance. This paper introduces two Context-awareness models, the macro and micro approaches, that define and integrate contextual views of individual pervasive components (micro level) and global knowledge of the system (macro level). These models are presented and compared with respect to their ability to meet the requirements of the Autonomic Pervasive Computing concept of the four selves.

The growth of ubiquitous computing and its integration in smart environments constitutes a major challenge. However, many issues related to this technology were raised, and many efforts have been made to facilitate the implementation of such technology. Information management, sharing and reuse are examples of these issues. Ontologies are used in this context as promising solution to deal with these issues. Nevertheless, ontologies developed to date in the context of smart environments are limited and do not address all aspects of smart environments such as the problem of referentiality and the environmental change. In this paper, we propose a universal ontology for smart environments aiming firstly to overcome the limitations of the existing ontologies, and secondly extend its capabilities by adding new environmental aspects such as those mentioned previously. In this paper, we also demonstrate how our ontology can be used to describe domains as well as applications.

The Autonomic Pervasive Computing can simplify the complexity of the configuration, maintenance and management of pervasive environments such as smart spaces. The Pervasive Computing is a paradigm where the information processes are distributed in the environment components, bringing processes more closer to the information context. On the other hand, the Autonomic Computing allows to simplify the complexity caused by the heterogeneity of the space components, there large number, the dynamic relations between them, etc.

Smart living spaces provide autonomy and assistance to people with disabilities and elderly people in order to increase their independence. In this paper we present our approach to personalize a complex living environment for users with special needs. It provides an appropriate middleware for supporting assistive services in dynamic environments. It is built upon formalisms based on Description logic (DL) named Semantic Matching Framework (SMF) and designed on the matching of two models: an environment model, and a user model. SMF was implemented and integrated into a demonstrator, which is validated under laboratory conditions. Performance results are presented to highlight the time response of SMF.