A Cluster–on–Demand Algorithm with Load Balancing for VANET

  • Yun Zheng
  • Yi Wu
  • Zhexin Xu
  • Xiao Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10036)


Cluster–on–Demand VANET clustering algorithm (CDVC) along with load balancing for urban is proposed. Urban vehicles are characterized by unpredictable moving direction. These challenges are solved by CDVC, which is composed of three main procedures; that is, initial clustering, cluster merging, and cluster head selection. In initial clustering, vehicles are clustered determines the boundary of each cluster. In cluster merging, Self–Organizing Maps (SOMs) is used for re–clustering by the similarity of nodes, which guarantees the stability of clusters. It leads to achieve load balancing. In cluster head selection, the information of location and mobility are combined to select a more stable cluster head. The performance of CDVC is evaluated and compared with the Lowest ID (LID) and Mobility based on clustering (MOBIC). Finally, the simulation results reveal that CDVC is superior to LID and MOBIC in terms of cluster head duration, clusters number, and load balancing.


Vehicular ad hoc networks Clustering Cluster–on–demand IEEE 802.11p Load balancing Urban environment 



The authors would like to thank Dr. Hsin–Chiu Chang for his help in the comments and preparation of this paper. This research was financially supported by the National Natural Science Foundation of China (No. 61571128), the Research Fund for the Doctoral Program of Higher Education of China (No. 20133503120 003), the Natural Science Foundation of Fujian Province (No. 2013J01224), the Key Projects of Science and Technology Plan for Industry of the Science and Technology Department of Fujian Province (No. 2014H0019), and it also was financially supported by the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT 15R10), the Education Department of Fujian Province Science and Technology Project (JB13005).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of EducationFujian Normal UniversityFuzhouPeople’s Republic of China
  2. 2.Fujian Provincial Key Laboratory of Photonics TechnologyFujian Normal UniversityFuzhouPeople’s Republic of China

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