A new spectral classification for robust clustering in wireless sensor networks

TitreA new spectral classification for robust clustering in wireless sensor networks
Publication TypeConference Paper
Year of Publication2013
AuthorsElbhiri, Bab, Fkihi, SEbc, Saadane, Rbd, Lasaad, Nb, Jorio, Ab, Aboutajdine, Db
Conference NameProceedings of 2013 6th Joint IFIP Wireless and Mobile Networking Conference, WMNC 2013
Abstract

Wireless sensor network has recently become an area of attractive research interest. It consists of low-cost, low power, and energy-constrained sensors responsible for monitoring a physical phenomenon and reporting to sink node where the end-user can access the data. Saving energy and therefore extending the wireless sensor network lifetime, involves great challenges. For these purposes, clustering techniques are largely used. Using many empirical successes of spectral clustering methods, we propose a new algorithm that we called Spectral Classification for Robust Clustering in Wireless Sensor Networks (SCRC-WSN). This protocol is a spectral partitioning method using graph theory technics with the aim to separate the network in a fixed optimal number of clusters. The cluster's nodes communicate with an elected node called cluster head, and then the cluster heads communicate the information to the base station. Defining the optimal number of clusters and changing dynamically the cluster head election probability are the SCRC-WSN strongest characteristics. In addition our proposed protocol is a centralized one witch take into account the node's residual energy to define the cluster heads. We studied the impact of node density on the robustness of the SCRC-WSN algorithm as well as its energy and its lifetime gains. Simulation results show that the proposed algorithm increases the lifetime of a whole network and presents more energy efficiency distribution compared to the Low-Energy Adaptive Clustering Hierarchy (LEACH) approach and the Centralized LEACH (LEACH-C)one. © 2013 IEEE.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84881393741&doi=10.1109%2fWMNC.2013.6548982&partnerID=40&md5=648ff469917070df5c7f54337dfa592b
DOI10.1109/WMNC.2013.6548982
Revues: 

Partenaires

Localisation


Location map

Suivez-nous sur

  

Contactez-nous

ENSIAS

Avenue Mohammed Ben Abdallah Regragui, Madinat Al Irfane, BP 713, Agdal Rabat, Maroc

Résultat de recherche d'images pour "icone fax" Télécopie : (+212) 5 37 77 72 30

    Compteur de visiteurs:280,151
    Education - This is a contributing Drupal Theme
    Design by WeebPal.