A Unified Framework of Clustering Approach in Vehicular Ad Hoc Networks

• Published in: IEEE transactions on intelligent transportation systems Vol. 19; no. 5; pp. 1401 - 1414
• Main Authors: Ren, Mengying, Zhang, Jun, Khoukhi, Lyes, Labiod, Houda, Veque, Veronique
• Format: Journal Article
• Language: English
• Published: IEEE 01-05-2018
• Subjects: Ad hoc networks; clustering; Clustering algorithms; Computer Science; Heuristic algorithms; Measurement; Networking and Internet Architecture; Prediction algorithms; stability; Stability criteria; Vehicular ad hoc networks; Measurement; Heuristic algorithms; Stability criteria; Clustering algorithms; Vehicular ad hoc networks; Prediction algorithms; Ad hoc networks; clustering; stability
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2017.2727226

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.