A Unified Framework of Clustering Approach in Vehicular Ad Hoc Networks

Abstract : Effective clustering algorithms are indispensable in order to solve the scalability problem in vehicular ad hoc networks. Although current existing clustering algorithms show increased cluster stability under some certain traffic scenarios, it is still hard to address which clustering metric performs the best. In this paper, we propose a unified framework of clustering approach (UFC), composed of three important parts: 1) neighbor sampling; 2) backoff-based cluster head selection; and 3) backup cluster head based cluster maintenance. Three mobility-based clustering metrics, including vehicle relative position, relative velocity, and link lifetime, are considered in our approach under different traffic scenarios. Furthermore, a detailed analysis of UFC with parameters optimization is presented. Extensive comparison results among UFC, lowest-ID, and VMaSC algorithms demonstrate that our clustering approach performs high cluster stability, especially under high dynamic traffic scenarios.
Type de document :
Article dans une revue
IEEE Transactions on Intelligent Transportation Systems, IEEE, 2017, pp.1-14. 〈http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8011498&isnumber=4358928〉. 〈10.1109/TITS.2017.2727226〉
Liste complète des métadonnées

https://hal-centralesupelec.archives-ouvertes.fr/hal-01580376
Contributeur : Véronique Vèque <>
Soumis le : vendredi 1 septembre 2017 - 14:10:29
Dernière modification le : jeudi 5 avril 2018 - 12:30:06

Identifiants

Citation

Jun Zhang, Lyes Khoukhi, Houda Labiod, Véronique Vèque, Mengying Ren. A Unified Framework of Clustering Approach in Vehicular Ad Hoc Networks. IEEE Transactions on Intelligent Transportation Systems, IEEE, 2017, pp.1-14. 〈http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8011498&isnumber=4358928〉. 〈10.1109/TITS.2017.2727226〉. 〈hal-01580376〉

Partager

Métriques

Consultations de la notice

355