UBC Theses and Dissertations

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UBC Theses and Dissertations

A platform for building context-aware mobile crowdsensing applications in vehicular social networks Hu, Xiping


In the past few years, many research works have demonstrated that mobile crowdsensing could be effectively applied in vehicular social networks (VSNs) to serve many purposes and bring huge economic benefits in transportation. In this thesis, we provide a crowdsensing platform which addresses the research challenges in the overall workflow of crowdsensing in VSNs in terms of task allocation and task execution. This platform supports the creation of different context-aware mobile crowdsensing applications and facilitates their real-world deployments in VSNs. First, because of the inherent nature of crowdsensing, usually a crowdsensing task needs a group of different participants to finish it collaboratively. Thus, for task allocation in crowdsensing, we propose an application-oriented service collaboration model (ASCM). This ASCM automatically allocates multiple participants with multiple crowdsensing tasks across different mobile devices and cloud platform in an efficient and effective manner in VSNs. Second, for task exaction of mobile crowdsensing applications in VSNs, the dynamic network connectivity of the underlying vehicular ad-hoc networks (VANETs) may cause failures of such applications during their executions. We design S-Aframe, an agent-based multi-layer framework, which provides a programming model to support creation and deployment of robust and reliable crowdsensing applications that self-adapt to the dynamic nature of VANETs. Furthermore, due to the dynamism of VANETs and the opportunism of user connections in VSNs, the changing environments of the users involved in the VSNs may also result in users’ dynamic contexts. We propose a context-aware semantic service (CSS), and incorporate this service with S-Aframe to improve the self-adaptiveness of mobile crowdsensing applications to users’ dynamic contexts of VSNs. Finally, we design and develop SAfeDJ, a crowdsensing-based situation-aware music recommendation application for drivers. The development of SAfeDJ has further demonstrated how our platform supports the creation of a context-aware mobile crowdsensing application, and facilitates the realization of such an application in real-world deployment in VSNs.

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Attribution-NonCommercial-NoDerivs 2.5 Canada