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Information Flow in Opportunistic Wireless Networks (INFLOW)
Date du début: 1 sept. 2011, Date de fin: 10 mai 2016 PROJET  TERMINÉ 

"The objective of INFLOW is to develop the methodology and algorithms for the modeling and analysis of optimal information flow in future wireless networks, thus leading to the design of new network protocols and algorithms. Building on recent breakthroughs in network information theory and in distributed systems, the aim is to leverage the potential of networks consisting essentially of wireless and mobile nodes.Advances in this area will have a significant impact on the possibilities of the future Internet. Portable devices with ubiquitous wireless connectivity have the potential to revolutionize the personal computing landscape, creating opportunities for unprecedented applications and services. But, standing in the way of this vision are challenges spanning the entire system architecture, due to the distinct characteristics of the wireless medium. To overcome such challenges, further research investigations focused on the understanding of the fundamental properties of complex wireless systems are needed.The first goal consists in pushing the theoretical performance limits of mobile and wireless networks, taking into account the state of the art results in network information theory, and thus focusing on two emerging key principles: opportunistic networking and network coding. Thus, we propose to study dynamically changing networks, i.e., networks where the structure, topology, and demands may vary in a short time scale as compared to the information transfer. The proposed interdisciplinary approach draws on advanced mathematical models, founded on information theory, stochastic analysis, the techniques developed for the analysis of algorithms, and statistical physics inspired methods to study the emerging behavior of large-scale networks. But, the goal is to deliver practical network solutions, in the form of new protocols, architectures and algorithms, which are derived from the insights provided by the modeling analysis, and offer optimized performance."

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