Context-aware Services for Mobile Devices: From Architecture Design to Empirical Inference

Currently, mobile devices are aware of user position, which can be provided to mobile apps for the development of tailored services known as Location-Based Services. Further advances on current Location-based Services (LBS), i.e. using any other information from the user such as gender, music preferences etc, may lead to transition from a Location-Based environment to a fully developed ContextAware environment.The current trend towards Context-aware Services (CAS) is reflected in academic research since more than twenty years as well as in the progress in Software Development Kits (SDKs) of the main mobile operating systems, where CAS frameworks are currently being used. However, there is no community agreement for modelling context CAS and little is known about the architecture of these context management frameworks of the mobile operating systems.Based on previous research in the area of CAS, I establish and analyse a reasoning architecture, the Context Engine (CE), that enables the main steps of designing and implementing context-aware services. The chief utility of CAS is their ability to formulate and encapsulate information, obtain user context through context acquisition tools and distribute it to third-party applications that build personalised services based on the provided information. The CE has the responsibility of selecting the optimal context acquisition tool to solve a concrete problem which is discussed in this dissertation.Furthermore, this thesis contributes to the development of context inference tools by studying two particular cases. The first case aims at inferring user (semantic) location information based on mobile phone usage data. This first case has been carried out in collaboration with Microsoft Finland, which provides a similar context inference solution to mobile developers through their Software Development Kit (SDK). The second case aims at inferring user information based on social network information, i.e. infer user information based on his or her connections. Both studies yield positive results and have the potential to be extended to obtain better context acquisition tools and, therefore, better user context