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Intelligent Clustering in Vehicular ad hoc Networks
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Intelligent Clustering in Vehicular ad hoc Networks


Abstract

A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.


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Cite this article

[IEEE Style]
F. Aadil, S. Khan, K. B. Bajwa, M. F. Khan and A. Ali, "Intelligent Clustering in Vehicular ad hoc Networks," KSII Transactions on Internet and Information Systems, vol. 10, no. 8, pp. 3512-3528, 2016. DOI: 10.3837/tiis.2016.08.005.

[ACM Style]
Farhan Aadil, Salabat Khan, Khalid Bashir Bajwa, Muhammad Fahad Khan, and Asad Ali. 2016. Intelligent Clustering in Vehicular ad hoc Networks. KSII Transactions on Internet and Information Systems, 10, 8, (2016), 3512-3528. DOI: 10.3837/tiis.2016.08.005.