Performance Evaluation of Dynamic Clustering of vehicles in VANET

Amudhavel, J.; Kumar, Krishan; Smrithi, R. Sai; Banumathi, S.; Rajaguru, D.; Vengattaraman, T.

doi:10.1145/2743065.2743123

Cited by 1 publication

(2 citation statements)

References 23 publications

(17 reference statements)

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“…Generally, these metrics are used in comparisons with other existing solutions. According to Fauziyyah et al, Amudhavel et al and Guizani, we have the following groups of metrics: Messages complexity: It is the number of control messages received by the nodes so that a valid cluster re‐organization may be performed after a change in the network topology. Network overhead: It is the number of messages related to the clustering process and the resources used by every node during network operation, such as bandwidth, energy, memory, and time. Cluster structure stability: Certain metrics in this group are described below. CH lifetime: It is the length of any maximal time interval during which a node is CH. The average CH lifetime is calculated by dividing the total CH lifetime by the total number of state changes from CH to another state. CM lifetime : It is the length of any maximal time interval during which a node is CM.…”

Section: Clustering In Vanets: An Overviewmentioning

confidence: 99%

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Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Senouci

Harous

Aliouat

2020

Int J Communication

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Summary Vehicular ad hoc networks (VANETs) have recently attracted considerable attention owing to their wide range of applications. However, there are several challenges, such as mobility, routing, scalability, quality of services, and security. Clustering is an important control mechanism in high‐mobility networks and has been verified to be a promising approach in VANETs as well, as it ensures a basic level of network performance. Accordingly, several clustering algorithms have been proposed for these networks, and different protocols typically focus on various performance metrics. In this study, we provide a thorough review of clustering algorithms in VANETs. First, we present background material regarding the clustering process. Secondly, we propose a new taxonomy that categorizes clustering algorithms in VANETs based on different design aspects and provides a description of the algorithms in each category. Thirdly, an analysis of the algorithms in each category is provided according to various comparison metrics. Fourthly, we highlight the main challenges for each category and discuss some open research issues. Finally, we provide a general comparison of different clustering algorithms according to selected key parameters. Thus, this study provides a more thorough understanding of VANET clustering algorithms and the research trends in this area.

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Section: Clustering In Vanets: An Overviewmentioning

confidence: 99%

“…Generally, these metrics are used in comparisons with other existing solutions. According to Fauziyyah et al, 34 Amudhavel et al 35 and Guizani, 36 we have the following groups of metrics:…”

Section: Performance Metrics For Clustering Algorithmsmentioning

confidence: 99%