A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs

Prior study suggests that VANET has two types of communications: Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. V2V is very important and ensures cooperative communications between vehicles and safety measures. It is also defined as Inter-Vehicle Communication (IVC).The...

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Main Authors: Akhilesh Bijalwan, Kamlesh Chandra Purohit, Preeti Malik, Mohit Mittal
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/11/19/3071
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author Akhilesh Bijalwan
Kamlesh Chandra Purohit
Preeti Malik
Mohit Mittal
author_facet Akhilesh Bijalwan
Kamlesh Chandra Purohit
Preeti Malik
Mohit Mittal
author_sort Akhilesh Bijalwan
collection DOAJ
description Prior study suggests that VANET has two types of communications: Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. V2V is very important and ensures cooperative communications between vehicles and safety measures. It is also defined as Inter-Vehicle Communication (IVC).The communication is based on clustering the nodes to transmit the data from vehicle to vehicle. The overhead and stability are considered as main challenges that need to be addressed during vehicle intersections. In this paper, a novel self-adaptable Angular based k-medoid Clustering Scheme (SAACS) is proposed to form flexible clusters. The clusters are formed by estimating the road length and transmission ranges to minimize the network delay. And the Cluster Head (CH) is elected from a novel performance metric, ‘cosine-based node uncoupling frequency,’ that finds the best nodes irrespective of their current network statistics. The parametric analysis varies according to the number of vehicular nodes with the transmission range. The experimental results have proven that the proposed technique serves better in comparison to existing approaches such as Cluster Head Lifetime (CHL), Cluster Member Lifetime (CML), Cluster Number (CL), Cluster Overhead (CO), Packet Loss Ratio (PLR) and Average Packet Delay (APD). CHL is enhanced 40% as compare to Real-Time Vehicular Communication (RTVC), Efficient Cluster Head Selection (ECHS) whereas CML is 50% better than RTVC and ECHS. Packet loss ratio and overhead is 45% better in our proposed algorithm than RTVC and ECHS. It is observed from the results that the incorporation of cosine-based node uncoupling frequency has minimized the incongruity between vehicular nodes placed in dense and sparse zones of highways.
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spelling doaj.art-0be204187ed84a16a5bf6dc489677c4f2023-11-23T20:05:48ZengMDPI AGElectronics2079-92922022-09-011119307110.3390/electronics11193071A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETsAkhilesh Bijalwan0Kamlesh Chandra Purohit1Preeti Malik2Mohit Mittal3Department of CSE, Graphic Era Deemed to Be University Dehradun, Uttarakhand 248002, IndiaDepartment of CSE, Graphic Era Deemed to Be University Dehradun, Uttarakhand 248002, IndiaDepartment of CSE, Graphic Era Deemed to Be University Dehradun, Uttarakhand 248002, IndiaCRIStAL, Institut National de Recherche en Informatique et en Automatique (INRIA), 59650 Lille, FrancePrior study suggests that VANET has two types of communications: Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. V2V is very important and ensures cooperative communications between vehicles and safety measures. It is also defined as Inter-Vehicle Communication (IVC).The communication is based on clustering the nodes to transmit the data from vehicle to vehicle. The overhead and stability are considered as main challenges that need to be addressed during vehicle intersections. In this paper, a novel self-adaptable Angular based k-medoid Clustering Scheme (SAACS) is proposed to form flexible clusters. The clusters are formed by estimating the road length and transmission ranges to minimize the network delay. And the Cluster Head (CH) is elected from a novel performance metric, ‘cosine-based node uncoupling frequency,’ that finds the best nodes irrespective of their current network statistics. The parametric analysis varies according to the number of vehicular nodes with the transmission range. The experimental results have proven that the proposed technique serves better in comparison to existing approaches such as Cluster Head Lifetime (CHL), Cluster Member Lifetime (CML), Cluster Number (CL), Cluster Overhead (CO), Packet Loss Ratio (PLR) and Average Packet Delay (APD). CHL is enhanced 40% as compare to Real-Time Vehicular Communication (RTVC), Efficient Cluster Head Selection (ECHS) whereas CML is 50% better than RTVC and ECHS. Packet loss ratio and overhead is 45% better in our proposed algorithm than RTVC and ECHS. It is observed from the results that the incorporation of cosine-based node uncoupling frequency has minimized the incongruity between vehicular nodes placed in dense and sparse zones of highways.https://www.mdpi.com/2079-9292/11/19/3071VANETsV2Vdynamic clusteringnetwork collisioncluster stabilityk-medoid clustering and node uncoupling frequency
spellingShingle Akhilesh Bijalwan
Kamlesh Chandra Purohit
Preeti Malik
Mohit Mittal
A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs
Electronics
VANETs
V2V
dynamic clustering
network collision
cluster stability
k-medoid clustering and node uncoupling frequency
title A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs
title_full A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs
title_fullStr A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs
title_full_unstemmed A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs
title_short A Self-Adaptable Angular Based K-Medoid Clustering Scheme (SAACS) for Dynamic VANETs
title_sort self adaptable angular based k medoid clustering scheme saacs for dynamic vanets
topic VANETs
V2V
dynamic clustering
network collision
cluster stability
k-medoid clustering and node uncoupling frequency
url https://www.mdpi.com/2079-9292/11/19/3071
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