Ph.D Computer Science

Université Paris-Est, Defended on 04 November 2016

Laboratoire d’Informatique Gaspard Monge - LIGM, University Paris-Est Marne-la-Vallée, France

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

Advisor: Prof. Yacine Ghamri-Doudane

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 de- mand, both in terms of cost and bandwidth that are necessary to handle the “massive” content generated and consumed by mobile users in an urban environment. This is partly due to the connection-centric nature of current mobile systems. 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 novel schemes, VISIT and SAVING. These schemes aim the efficient collection and storage of content at vehicles, closer to the urban mobile user, to reduce bandwidth demand 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 recruit- ing the vehicles, which are adequate to perform collaborative content caching in an urban environment. VISIT and SAVING are simulated for about 2986 vehicles with realistic urban mobility traces. Comparison results with other schemes in the literature suggest that both are not only efficient but also scalable data collection and storage systems.