Assisting V2V failure recovery using Device-to-Device Communications

This paper aims to propose a new solution for failure recovery (dead-ends) in Vehicle to Vehicle (V2V) communications through LTE-assisted Device-to-Device communications (D2D). Based on the enhanced networking capabilities offered by Intelligent Transportation Systems (ITS) architecture, our solution can efficiently assist V2V communications in failure recovery situations. We also derive an analytical model to evaluate generic V2V routing recovery failures. Moreover, the proposed hybrid model is simulated and compared to the generic model under different constrains of worst and best cases of D2D discovery and communication. According to our comparison and simulation results, the hybrid model decreases the delay for alarm message propagation to the destination (typically the Traffic Control Center TCC) through the Road Side Unit (RSU)Comment: 3 page

A Hybrid Model to Extend Vehicular Intercommunication V2V through D2D Architecture

In the recent years, many solutions for Vehicle to Vehicle (V2V) communication were proposed to overcome failure problems (also known as dead ends). This paper proposes a novel framework for V2V failure recovery using Device-to-Device (D2D) communications. Based on the unified Intelligent Transportation Systems (ITS) architecture, LTE-based D2D mechanisms can improve V2V dead ends failure recovery delays. This new paradigm of hybrid V2V-D2D communications overcomes the limitations of traditional V2V routing techniques. According to NS2 simulation results, the proposed hybrid model decreases the end to end delay (E2E) of messages delivery. A complete comparison of different D2D use cases (best & worst scenarios) is presented to show the enhancements brought by our solution compared to traditional V2V techniques.Comment: 6 page

Coalition Formation Algorithm of Prosumers in a Smart Grid Environment

In a smart grid environment, we study coalition formation of prosumers that aim at entering the energy market. It is paramount for the grid operation that the energy producers are able to sustain the grid demand in terms of stability and minimum production requirement. We design an algorithm that seeks to form coalitions that will meet both of these requirements: a minimum energy level for the coalitions and a steady production level which leads to finding uncorrelated sources of energy to form a coalition. We propose an algorithm that uses graph tools such as correlation graphs or clique percolation to form coalitions that meet such complex constraints. We validate the algorithm against a random procedure and show that, it not only performs better in term of social welfare for the power grid, but also that it is more robust against unforeseen production variations due to changing weather conditions for instance.Comment: 6 pages, 4 figures, 1 table. submited to ICC 201

Sur l'adaptation au contexte des réseaux de capteurs sans fil

Mobiles, pouvant changer d environnements au cours du temps, et de milieu pour la transmission des données et de forme de topologie, les capteurs doivent s adapter au contexte où ils se trouvent afin d optimiser les mécanismes qu ils mettent en œuvre. Dans la première partie, nous proposons un mécanisme pour adapter l architecture d un réseau de capteurs dynamiquement en fonction du contexte et comprenant la détection dynamique d un changement de contexte, la détection dynamique du nouveau, l adaptation dynamique au niveau des trois couches responsables de la gestion des liens de communication en conséquence, le tout sous contrainte de consommation d énergie. Le travail mené dans cette première partie a d emblée posé la question de la détection du contexte. C est une question assez difficile car elle est mal définie. L objet de la deuxième partie est d aborder la reconnaissance à la volée de la technologie utilisée par les réseaux émettant du trafic concurrent au réseau de capteurs. Le mécanisme proposé, FIM, identifie la cause d interférences à partir de modèles d erreurs observées dans les paquets de données. La détection du contexte permet aux nœuds du réseau de capteurs d obtenir des informations sur l environnement. Certains nœuds doivent avoir une connaissance plus fiable de l environnement que d autres. Comment récupérer l information de nœuds voisins, sélectionner ceux de qui on la récupère et ne garder que ce qui nous semble sûr et utile sont les questions qui sont abordées dans la troisième partie. Nous proposons un mécanisme qui permet de décider dynamiquement si des mécanismes de docition doivent être utilisés ou pasBeing mobile, the wireless sensors must adapt to the changing environment. Therefore, in the first part of this thesis we propose a mechanism to adapt the WSN architecture dynamically based on the detected context; this includes the dynamic detection of the topology change, the detection of the new context and consequently the dynamic adaptation of the communication layer. All of these actions are executed under constraints on energy consumption. The work done in this part poses the question of detecting the new context. This is a rather difficult question because it is unclear. The purpose of the second part is to detect on the fly the type of the competitor technology generating a traffic that interferes with the WSN. The proposed mechanism, FIM, identifies the cause of interference from errors model observed in the corrupt data packets. The context detection allows the nodes of the sensor network to obtain information about the environment. Some nodes must have more reliable information on the environment than others. How to retrieve the information? From which neighboring nodes? And what information to keep as safe and useful? Are the questions that are addressed in the third part. We propose a mechanism to dynamically decide if docition mechanisms should be used or notEVRY-INT (912282302) / SudocSudocFranceF

Energy management for electric vehicles in smart cities: a deep learning approach

International audienceWe propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajec-tory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy managemen