E
Eric Renault
Researcher at ESIEE Paris
Publications - 129
Citations - 742
Eric Renault is an academic researcher from ESIEE Paris. The author has contributed to research in topics: Routing protocol & The Internet. The author has an hindex of 12, co-authored 129 publications receiving 637 citations. Previous affiliations of Eric Renault include Université Paris-Saclay & Institut Mines-Télécom.
Papers
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Journal ArticleDOI
Cytosine excited state dynamics studied by femtosecond fluorescence upconversion and transient absorption spectroscopy
TL;DR: In this paper, a femtosecond spectroscopic study of the DNA base cytosine in aqueous solution at room temperature was performed and two different experimental techniques were used, fluorescence upconversion and transient absorption, providing complementary information on the excited state relaxation.
Proceedings ArticleDOI
Scenarios and Research Issues for a Network of Information
Christian Dannewitz,Kostas Pentikousis,Rene Rembarz,Eric Renault,Ove Strandberg,Javier Ubillos +5 more
TL;DR: This paper describes ideas and items of work within the framework of the EU-funded 4WARD project, and explains how a new networking paradigm emerges, by adopting the information-centric network architecture approach, which is called Network of Information (NetInf).
Proceedings ArticleDOI
A Hybrid Authentication and Key Establishment Scheme for WBAN
TL;DR: This paper proposes a hybrid authentication and key agreement scheme where symmetric cryptography is used in sensor/actuator nodes while identity-based cryptography concept is used between smartphone (MN) and the storage site (SS).
Proceedings ArticleDOI
New energy saving mechanisms for mobile ad-hoc networks using OLSR
TL;DR: Two new mechanisms to allow a fair power consumption in mobile ad-hoc networks using the OLSR routing protocol are proposed to introduce new MPR selection criteria which take into account the energy capacity of nodes.
Proceedings ArticleDOI
CSI-MIMO: K-nearest Neighbor applied to Indoor Localization.
TL;DR: The Knearest neighbor method presented in this paper achieves a Mean Square Error (MSE) of 2.4 cm which outperforms its counterparts and is compared with three other methods all based on deep learning approaches and tested with the same dataset.