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IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Bagging Ensemble X-Means Clustering Based Multicriteria Whale Optimized Data Dissemination In Vanet

[Full Text]

 

AUTHOR(S)

D.Radhika, A.Bhuvaneswari

 

KEYWORDS

Bagging Ensemble X-Means Clustering, Data Dissemination, Multi-criteria Whale Optimization, Optimal Cluster Head, Routing and Vehicle Nodes.

 

ABSTRACT

Vehicular Ad-Hoc Network (VANET) is an intelligent transportation system owing to the rapid extension of safety and multimedia applications. But, successful data dissemination and reliable routing in VANET is still a difficult problem. In order to overcome such drawbacks, Bagging Ensemble X-Means Clustering Based Multi-criteria Whale Optimization (BEXCMWO) technique is proposed. At first, BEXCMWO technique applies Bagging Ensemble X-Means Cluster-based Routing (BEXC-R) where the total network is split into a number of groups. Each group contains the number of vehicle nodes. The BEXC-R uses the X-means clustering as a weak cluster to group each vehicle to the closest cluster centroid based on the vehicle’s density, direction, distances, and velocities. After finishing the clustering process, vehicle node with the minimum average distance among cluster members is selected as the cluster head in BEXCMWO technique for route the data from source to destination. Subsequently, the optimal nearest cluster head is identified with the help of Multi-criteria Whale Optimization-based Data Dissemination (MWO-DD) for distributing the data packets from the source vehicle node to destination vehicle node in VANET. The simulation of BEXCWO technique is conducted using parameters such as reliability, end to end delay and throughput. The simulation results show that the BEXCMWO technique is able to increases the reliability and also reduces the end to end delay of data dissemination in VANET when compared to state-of-the-art methods

 

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