Semantic interoperability framework for smart spaces: Dissertation

At the heart of the smart space vision is the idea that devices interoperate with each other autonomously to assist people in their everyday activities. In order to make this vision a reality, it is important to achieve semantic-level interoperability between devices. The goal of this dissertation is to enable Semantic Web technology-based interoperability in smart spaces. There are many challenges that need to be solved before this goal can be achieved. In this dissertation, the focus has been on the following four challenges: The first challenge is that the Semantic Web technologies have neither been designed for sharing real-time data nor large packets of data such as video and audio files. This makes it challenging to apply them in smart spaces, where it is typical that devices produce and consume this type of data. The second challenge is the verbose syntax and encoding formats of Semantic Web technologies that make it difficult to utilise them in resource-constrained devices and networks. The third challenge is the heterogeneity of smart space communication technologies that makes it difficult to achieve interoperability even at the connectivity level. The fourth challenge is to provide users with simple means to interact with and configure smart spaces where device interoperability is based on Semantic Web technologies. Even though autonomous operation of devices is a core idea in smart spaces, this is still important in order to achieve successful end-user adoption. The main result of this dissertation is a semantic interoperability framework, which consists of following individual contributions: 1) a semantic-level interoperability architecture for smart spaces, 2) a knowledge sharing protocol for resource-constrained devices and networks, and 3) an approach to configuring Semantic Web-based smart spaces. The architecture, protocol and smart space configuration approach are evaluated with several reference implementations of the framework components and proof-of-concept smart spaces that are also key contributions of this dissertation.