Cluster-based data dissemination, cluster head formation under sparse, and dense traffic conditions for vehicular ad hoc networks

2018 ◽  
Vol 31 (8) ◽  
pp. e3533 ◽  
Author(s):  
Kashif Naseer Qureshi ◽  
Abdul Hanan Abdullah ◽  
Faisal Bashir ◽  
Saleem Iqbal ◽  
Khalid Mahmood Awan
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Data Dissemination ◽  
Cluster Head ◽  
Traffic Conditions ◽  
Head Formation ◽  
Hoc Networks

Data dissemination in urban Vehicular Ad hoc Networks with diverse traffic conditions

2013 ◽  
Author(s):  
Guilherme Maia ◽  
Leandro A. Villas ◽  
Azzedine Boukerche ◽  
Aline C. Viana ◽  
Andre L. L. Aquino ◽  
...  
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Data Dissemination ◽  
Traffic Conditions ◽  
Hoc Networks

scholarly journals An Efficient Data Transmission in VANET Using Clustering Method

2017 ◽  
Vol 63 (3) ◽  
pp. 309-313 ◽  
Author(s):  
C. Suganthi Evangeline ◽  
S. Appu
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Cluster Head ◽  
Data Gathering ◽  
Link Quality ◽  
Clustering Methods ◽  
The Road ◽  
Hoc Networks

Abstract A special type of Mobile Ad-hoc Networks (MANETs) which has frequent changes of topology and higher mobility is known as Vehicular Ad-hoc Networks (VANETs). In order to divide the network into groups of mobile vehicles and improve routing, data gathering, clustering is applied in VANETs. A stable clustering scheme based on adaptive multiple metric combining both the features of static and dynamic clustering methods is proposed in this work. Based on a new multiple metric method, a cluster head is selected among the cluster members which is taken from the mobility metrics such as position and time to leave the road segment, relative speed and Quality of Service metrics which includes neighborhood degree, link quality of the RSU and bandwidth. A higher QoS and cluster stability are achieved through the adaptive multiple metric. The results are simulated using NS2 and shows that this technique provides more stable cluster structured with the other methods.

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