QoS-compliant Data Aggregation for Smart Grids - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

QoS-compliant Data Aggregation for Smart Grids

Résumé

The Smart Grid (SG) aims to transform the current electric grid into a "smarter" network where the integration of renewable energy resources, energy efficiency and fault tolerance are the main benefits. A Wireless Sensor Network (WSN) controlling and exchanging messages across the grid is a promising solution because of its infrastructure free and ease of deployment characteristics. This comes at the cost of resource constrained and unstable links for such networks. The management of communication is then an issue: billions of messages with different sizes and priorities are sent across the network. Data aggregation is a potential solution to reduce loads on the communication links, thus achieving a better utilization of the wireless channel and reducing energy consumption. On the other hand, SG applications require different Quality of Service (QoS) priorities. Delays caused by data aggregation must then be controlled in order to achieve a proper communication. In this paper, we propose a work in progress, that consists of a QoS efficient data aggregation algorithm with two aggregation functions for the different traffics in a SG network. We expect to reduce the energy consumption while respecting the data delivery delays for the different SG applications.
Fichier principal
Vignette du fichier
Idea_paper__ENERGY.pdf (85.32 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01744647 , version 1 (03-07-2018)

Identifiants

  • HAL Id : hal-01744647 , version 1

Citer

Jad Nassar, Nicolas Gouvy, Nathalie Mitton. QoS-compliant Data Aggregation for Smart Grids. ENERGY 2018 - The Eighth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies, May 2018, Nice, France. ⟨hal-01744647⟩

Collections

INRIA INRIA2
234 Consultations
128 Téléchargements

Partager

Gmail Facebook X LinkedIn More