Profile Management Technology for Smart Customizations in Private Home Applications

The primary goal of smart homes is to provide their users with the maximum comfort and convenience. In this paper, we present a profile management framework for situation-dependent customization in smart home environments, which meet the user preference with given device capabilities. We apply profile processing and evolution methods to customize profiles on the fly and to automatically evolve, e.g., user preferences on demand. Furthermore, we give a comprehensive study on profile management technology. 1

Supporting policy-based contextual reconfiguration and adaptation in ubiquitous computing

In order for pervasive computing systems to be able to perform tasks which support us in everyday life without requiring attention from the users of the environment, they need to adapt themselves in response to context. This makes context-awareness in general, and context-aware adaptation in particular, an essential requirement for pervasive computing systems. Two of the features of context-awareness are: contextual reconfiguration and contextual adaptation in which applications adapt their behaviour in response to context. We combine both these features of context-awareness to provide a broad scope of adaptation and put forward a system, called Policy-Based Contextual Reconfiguration and Adaptation (PCRA) that provides runtime support for both. The combination of both context-aware reconfiguration and context-aware adaptation provides a broad scope of adaptation and hence allows the development of diverse adaptive context-aware applications. However, another important issue is the choice of an effective means for developing, modifying and extending such applications. The main argument forming the basis of this thesis is that we advocate the use of a policy-based programming model and argue that it provides more effective means for developing, modifying and extending such applications. This thesis addresses other important surrounding issues which are associated with adaptive context-aware applications. These include the management of invalid bindings and the provision of seamless caching support for remote services involved in bindings for improved performance. The bindings may become invalid due to failure conditions that can arise due to network problems or migration of software components, causing bindings between the application component and remote service to become invalid. We have integrated reconfiguration support to manage bindings, and seamless caching support for remote services in PCRA. This thesis also describes the design and implementation of PCRA, which enables development of adaptive context-aware applications using policy specifications. Within PCRA, adaptive context-aware applications are modelled by specifying binding policies and adaptation policies. The use of policies within PCRA simplifies the development task because policies are expressed at a high-level of abstraction, and are expressed independently of each other. PCRA also allows the dynamic modification of applications since policies are independent units of execution and can be dynamically loaded and removed from the system. This is a powerful and useful capability as applications may evolve over time, i.e. the user needs and preferences may change, but re-starting is undesirable. We evaluate PCRA by comparing its features to other systems in the literature, and by performance measures

Learning preferences for personalisation in a pervasive environment

With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations. This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users

Étude et conception d'un système de personnalisation et d'aide fonctionnelle multi-agents permettant d'assister simultanément de manière transparente les activités de vie quotidienne de multiples personnes dans un Habitat Intelligent pour la Santé

The application domains of this thesis are Health Smart Homes, and the research is more precisely centered on the improvement of daily-living for cognitively impaired persons and theirs caregivers.The proposed system can observe the context of each person, personalize the environment and assist the tasks detected if they need to be. Every action of the system is as unobtrusive as possible and takes into consideration the presence of more than one person. To personalize and assist the daily-living activities of a lone person, we need to know his personal context. This context is the conjunction of the preferences and habits, the illness or impairment, the movements in the smart home and the state of the various sensor and electrical devices, and the current activities that are detected for one person. To be able to assist many persons simultaneously, we need to compute the overall conjunction of each and every person's context since every presence can influence the global context and every personal one. This complexity brings a lot of problems like the multiple person localization and identification, or the personalization and assistance of multiple persons in the same space with various activities. Those problems are even more interesting since, following an ethical choice to ensure inhabitant's privacy, this project avoid the use of some intrusive technologies