Distributed Immersive Participation Realising Multi-Criteria Context-Centric Relationships on an Internet of Things
Abstract: Advances in Internet-of-Things integrate sensors and actuators in everyday items or even people transforming our society at an accelerated pace. This occurs in areas such as agriculture, logistics, transport, healthcare, and smart cities and has created new ways to interact with and experience entertainment, (serious) games, education, etc. Common to these domains is the challenge to realize and maintain complex relations with any object or individual globally, with the requirement for immediacy in maintaining relations of varying complexity. Existing architectures for maintaining relations on the Internet, e.g., DNS and search engines are insufficient in meeting these challenges. Their deficiencies mandate the research presented in this dissertation enabling the maintenance of dynamic and multi-criteria relationships among entities in real-time in an Internet-of-Things while minimizing the overall cost for maintaining such context-centric relationships. A second challenge is the need to represent nearness in context-centric relationships, since solutions need to build on what is closely related.The dissertation shows that the proximity on relations can be used to bring about the scalability of maintaining relationships across the IoT. It successfully demonstrates the concept and feasibility of self-organizing context-centric overlay networks for maintaining scalable and real-time relationships between endpoints co-located with associated physical entities. This is complemented by an object model for annotating objects and their relationships as derived and defined over the underpinning context interactions. Complementing measures of nearness are added through a non-metric multi-criteria approach to evaluating the notion of context proximity. A query language and an extension to the publish-subscribe approaches achieves distributed support for discovering such relationships; locating entities relative to a defined hyper-sphere of interest. Furthermore, it introduces adaptive algorithms for maintaining such relationships at minimal overall costs. The results demonstrate the feasibility of moving towards context-centric approaches to immersion and that such approaches are realizable over vast and distributed heterogeneous collections of user and their associated context information.
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