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Clustering in Vehicular Ad-Hoc Network Using Artificial Neural Network

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Vehicular Ad-Hoc Network (VANET) is an emerging field of wireless networks and one of the most promising applications of communication among vehicles. In particular, several moving vehicles without the pre-existing infrastructure to communicate, are currently the object of increased attention by researchers, to improve safety on the roads. In VANET, clustering routing protocols play a very important role in relation to the efficient usage of bandwidth distribution of resources and scalability. The clustering routing approach reduces the size of the routing table based on the used clustering structure. However, this algorithm suffers from the instability of network and discontinuous connectivity. This work presents a novel clustering algorithm based on mobility and reliability of vehicles for VANET, from which clusters use an artificial neural network system (ANN) in a distributed manner. The proposed algorithm considers the reliability rate value, speed and distance’s difference among the nodes in the cluster formation and the degree of learning for electing a cluster-head (CH). These parameters increase the stability and the connectivity and it can reduce the bandwidth and the end-to-end delay in the network. Experimental results show that the proposed protocol outperforms the existing solutions in terms of average CH Lifetime, average lifetime of the cluster, average membership lifetime, percentage of selecting abnormal vehicles a CH, bandwidth, and end-to-end delay.
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VANET; Clustering Routing Protocols; Stability; Cluster-Head; Reliability; Artificial Neural Network

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