Large scale data collection and storage using smart vehicles : An information-centric approach

Abstract : The growth in the number of mobile devices today result in an increasing demand for large amount of rich multimedia content to support numerous applications. It is however challenging for the current cellular networks to deal with such increasing demand, both in terms of cost and bandwidth for the ``massive'' content generated and consumed by mobile users in an urban environment due to its connection-centric nature. The technological advancement in modern vehicles allow us to harness their computing, caching and communication capabilities to supplement infrastructure network. It is now possible to recruit smart vehicles to collect, store and share heterogeneous data on urban streets in order to provide citizens with different services. Therefore, we leverage the recent shift towards Information Centric Networking (ICN) to introduce two schemes, VISIT and SAVING for the efficient collection and storage of content at vehicles, closer to the urban mobile user to avoid bandwidth and cost. VISIT is a platform which defines novel centrality metrics based on the social interest of urban users to identify and select the appropriate set of best candidate vehicles to perform urban data collection. SAVING is a social-aware data storage system which exploits complex networks to present game-theoretic solutions for finding and recruiting vehicles adequate to perform collaborative content caching in an urban environment. VISIT and SAVING are simulated for around 2986 vehicles with realistic urban mobility traces and comparison results with other schemes in literature suggest both not only efficient but also scalable data collection and storage systems
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Junaid Khan. Large scale data collection and storage using smart vehicles : An information-centric approach. Computation and Language [cs.CL]. Université Paris-Est, 2016. English. ⟨NNT : 2016PESC1045⟩. ⟨tel-01538308⟩

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