Situation Aware Mobile Apps Framework
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Mobile devices, like smart phones or tablets, have become ubiquitous, with their adoption being driven by their immediacy and sensing capabilities. Applications, or apps, that run on portable computing devices have surged in popularity, with billions of downloads taking place. However, an increasing number of mobile apps and their diverse users make it difficult to select the correct app to respond to evolving situations. To address this issue, it is vitally important to find an intelligent approach to provide situation awareness capabilities and an immediate response to the changes. In this thesis, we have developed a semantic framework for mobile apps named the Situation Awareness Mobile Apps Framework (SAMAF) to achieve the goal of dynamic and adaptive apps for automated composition, adaptation, and evolution of software systems responding to the mobile users' context and environmental changes. SAMAF is composed of two major components: i) a cloud based service framework for mobile apps development, deployment, and adaptation using a design of dynamic patterns for Service Oriented Architecture and ii) an ontology-based context modeling and reasoning framework that is implemented based on Context Ontology modeling and Event Condition Action (ECA) rule based inference to align the adaptation with the changes. The SAMAF framework has been evaluated by two kinds of experiments. One was conducted in real phone settings to obtain the running performance of mobile apps adapting to dynamic changes of the users' contexts. The other was performed with a large number of mobile phone users in a simulated JADE (Java Agent DEvelopment Framework), multiple agents' platform for testing the adaptability, reasoning correctness, and scalability based on the communication and reasoning capabilities among different kinds of agents. Our results show that the proposed framework supports feasible, scalable and adaptive responds to evolving contexts.
Table of Contents
Introduction -- Related work -- SAMAF model -- SAMAF implementation -- Scenario illustration -- Conclusion and future work