Vehicles as Big Data Carriers: Road Map Space Reduction and Efficient Data Assignment

Benjamin Baron 1 Prométhée Spathis 1 Hervé Rivano 2 Marcelo Dias de Amorim 1, 3
1 NPA - Networks and Performance Analysis
LIP6 - Laboratoire d'Informatique de Paris 6
2 URBANET - Réseaux capillaires urbains
CITI - CITI Centre of Innovation in Telecommunications and Integration of services, Inria Grenoble - Rhône-Alpes
3 CNRS - Centre National de la Recherche Scientifique
Abstract : We advocate the use of a data shuttle service model to offload bulk transfers of delay-tolerant data from the Internet onto standard vehicles equipped with data storage capabilities. We first propose an embedding algorithm that computes an offloading overlay on top of the road infrastructure. The goal is to simplify the representation of the road infrastructure as raw maps are too complex to handle. In this overlay, each logical link maps multiple stretches of road from the underlying road infrastructure. We formulate then the data transfer assignment problem as a novel linear programming model that determines the most appropriate logical paths in the offloading overlay for a data transfer request. We evaluate our proposal using actual road traffic counts in France. Numerical results show that we can satisfy weekly aggregate requests in the petabyte range while achieving cumulative bandwidth above 10 Gbps with a market share of 20% and only one terabyte of storage per vehicle.
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Communication dans un congrès
VTC2014-Fall - IEEE 80th Vehicular Technology Conference, Sep 2014, Vancouver, Canada. IEEE, pp.1-5, 2014, 〈10.1109/VTCFall.2014.6966227〉
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Soumis le : jeudi 22 mai 2014 - 14:27:49
Dernière modification le : mercredi 11 avril 2018 - 01:52:09
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Benjamin Baron, Prométhée Spathis, Hervé Rivano, Marcelo Dias de Amorim. Vehicles as Big Data Carriers: Road Map Space Reduction and Efficient Data Assignment. VTC2014-Fall - IEEE 80th Vehicular Technology Conference, Sep 2014, Vancouver, Canada. IEEE, pp.1-5, 2014, 〈10.1109/VTCFall.2014.6966227〉. 〈hal-00994848〉

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