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Intelligent Clustering in Vehicular and Ad Hoc Networks

Thesis Info

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External Link

Author

Farhan Aadil

Program

PhD

Institute

University of Engineering and Technology

City

Taxila

Province

Punjab

Country

Pakistan

Thesis Completing Year

2016

Thesis Completion Status

Completed

Subject

Computer Science

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/14005/1/Farhan_Aadil_Computer_Engineering_2016_HSR_UET_Taxila_05.01.2017.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727777822

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A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. VANETs have specific characteristics that impose new challenges to the network development and operation when compared with traditional mobile ad hoc networks (MANETs). Unlike traditional networks, where nodes are either static or move independently with low speeds, nodes in VANETs move with very high speeds, causing network fragmentations and rapid changes in the network topology. Additionally, the movement of vehicular nodes is dependent on driver behaviors and the interaction with neighboring vehicles. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Node clustering is a network management strategy in which nearby nodes are grouped into a set called cluster. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The CH may elect some of its CNs as gateway nodes that facilitate the inter-cluster communications among neighboring clusters. The lifetime of clusters and number of CHs determines the efficiency of network. In this thesis a clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like multi-objective particle swarm optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Moreover, some limitations in recent works have been identified due to which ACO based CACONET appears to be a better method. PSO based solution works fine for continuous values parameters. ACO based solutions works fine for combinatorial optimization problem. Clustering is basically combinatorial optimization problem and thus CACONET is more suitable for clustering as compared to PSO (as was used by competitors MOPSO and CLPSO). The comparative effectiveness of these algorithms is evaluated by varying the grid size of the network, the transmission range of nodes, and number of nodes in the network. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.
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