Personalized adaptation in pervasive systems via non-functional requirements

Pervasive environments are socio-technical systems that support the daily routines of their users in an invisible and unobtrusive manner. These systems are aware of and adapt to both the operational context and the characteristics and preferences of their users. Designing adaptation mechanisms that guarantee maximal user satisfaction is challenging, due to the inherent differences between users and the changing context where the system operates. In order to tackle this problem, we propose an approach that compares alternative system behaviors in terms of how well they satisfy the preferences of the current user concerning Non-Functional Requirements (NFRs) such as efficiency, comfort, energy saving, etc. Specifically, we propose a model-driven framework in which the models represent the user routines that the pervasive system helps to achieve. These routines include variability points, thereby enabling their behavior to be adapted at runtime in order to fit the context and the user preferences over NFRs. Our contributions include: (1) user-adaptive task models, a modeling language to describe user routines that accounts for user preferences over NFRs; (2) algorithms that use our models at runtime to guide a pervasive system in adapting its behavior to user preferences and context; and (3) an implementation and evaluation of our techniques.