Clustering algorithm in VANETs: A survey

Yang, Penghui; Wang, Jianxin; Zhang, Yangxian; Tang, Zinan; Song, Shengxi

doi:10.1109/icasid.2015.7405685

Cited by 19 publications

(14 citation statements)

References 19 publications

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“…Several taxonomies for clustering algorithms have been proposed, where the algorithms are categorized based on different metric parameters. According to various key parameters, Bali et al proposed a classification into six categories: predictive clustering including three subcategories: position‐based, destination‐based, and lane‐based algorithms; backbone‐based clustering including k ‐hop based algorithms, medium access control (MAC)‐based clustering including Institute of Electrical and Electronics Engineers (IEEE) 802.11 MAC‐based, time‐division multiple access (TDMA)‐based, and space‐division multiple access (SDMA)‐based algorithms; traditional clustering including active and passive algorithms, hybrid clustering including intelligence‐based, distributed, and driver behavior‐based algorithms; and secure clustering including authentication‐based algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…According to the clustering application, Cooper et al proposed a classification into eight classes: general purpose, routing, channel access management, security, QoS, traffic safety, and topology discovery applications, as well as a combination with cellular infrastructure applications. A survey of clustering protocols in VANETs can be found in Yang et al, where objectives, challenges, and issues were discussed. Moreover, the authors compared these protocols according to various parameters, such as relative velocity, node density, cluster size, hop distance, and cluster establishment methodology.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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“…Clustering has been widely studied in VANETs: for clustering of vehicles (Chai et al, 2014;Hadded et al, 2015;Hussain and Bingcai, 2017;Rawashdeh and Mahmud, 2012;Yang et al, 2015) and improving routing (Cooper et al, 2017;Dawande et al, 2015;Malathi and Sreenath, 2017) and security (Bansal et al, 2016). The current work is in a way different from these works, as it clusters the messages rather than the vehicles, and focuses on feature selection, intuitive analysis and new approach to prototype machine learning algorithms in Veins (Sommer et al, 2011).…”

Section: Clusteringmentioning

confidence: 99%

Performance analysis of clustering for message classification and congestion control in DSRC/WAVE-based vehicular ad-hoc networks

Reddy

Ramanathan

2019

IJVICS

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Vehicular Ad-Hoc Networks (VANETs) play a significant role in Intelligent Transportation Systems (ITS). The vehicles exchange awareness and safety information with surrounding vehicles using IEEE 802.11p Dedicated Short Range Communications (DSRC) and IEEE 1609 Wireless Access in Vehicular Environment (WAVE)-based short-range communication networks in 5.9 GHz band. These safety messages include periodic beacon messages and event driven emergency messages that are shared in Control Channel (CCH). By collecting these messages and applying clustering we can facilitate new applications like network congestion control, traffic control etc. The partitioning-based clustering methods like k-means and the Partitioning Around Medoids (PAM) are well known in data science. This paper focuses on understanding the message clustering intuitively. The clustering quality is measured using silhouette plot and average silhouette width. The simulation is carried out in OMNeT++ and SUMO-based Veins framework. A simple approach using a package called RInside is explored for fast prototyping of machine learning algorithms in OMNeT++ simulations.

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“…There were various clustering methods proposed in MANET and VANET. () They aimed to (1) reuse network resources, (2) ensure topology efficiency and stability, (3) save unnecessary bandwidth usage, etc. Vehicular ad hoc network is a subset of MANET, which contains vehicle nodes, roadside units, and the server.…”

Section: Related Workmentioning

confidence: 99%

K‐MING: A mobile proxy handoff control scheme for proximate group‐based geodata sharing

Huang

Ming

2017

Softw Pract Exp

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This work adopts a mobile social network in proximity (MSNP)-centric group touring service concept to share geo-points of interest's (POIs') data among mobile users belonging to the same touring group. In a group-based touring service, a mobile user can create a group in the MSNP that can consist of some clusters in which some members having the 3G/3.5G/4G cellular network service are selected as cluster leaders to enable their Wi-Fi hot spot functions as mobile proxies. After that, each group member can join one of the clusters by connecting with a cluster leader's Wi-Fi hot spot. Thereafter, each cluster leader downloads geo-POIs' data using 3G/3.5G/4G cellular network and then shares the downloaded POIs' data with its cluster members through its Wi-Fi hot spot. This work proposed a handoff mechanism such that each mobile user can handoff from its currently connected cluster to the other one that belongs to the same MNSP group. The main handoff concern is that each handheld device only allows a limited number of handheld devices to connect with its Wi-Fi hot spot at the same time. This work proposed a K-member-limited MSNP-based INter Group handoff scheme called K-MING to tackle the handoff problem. The performance analysis shows that K-MING for the group touring scenario has better performance for the sharing of geo-POIs' data downloading in terms of average power consumption and average downloaded data volume from 3G/3.5G/4G cellular network than that of the individual touring scenario. KEYWORDSclustering mechanism, data sharing, group-based touring service, location-based service, mobile social networks in proximity 600 /journal/spe Softw Pract Exper. 2018;48:600-620. HUANG AND MAI 601POI. 1,2 Two types of the LBS touring service are (1) individual-based and (2) group-based. The individual-based touring system considers issues that may exist when 1 person is using the LBS system for helping his touring. 3 The group-based touring system considers issues that may exist when a number of persons are using the LBS system to tour together. [4][5][6] Mobile social networks (MSNs), which are similar to online social networks, have raised enormous research studies. 7,8 Mobile social network integrates social networks and wireless communications in the mobile environments. It can be considered as an application or system that creates virtual communities and provides various contents among mobile users, for which mobile users can join any community that was created by other mobile users using any mobile device. Proximity service is a new service that allows neighboring mobile devices to use device-to-device (D2D) or terminal-to-terminal communication to share data with each other. 9,10 Some MSNs consider proximity, ie, nearby mobile users are interacting with each other directly using the D2D/terminal-to-terminal communication technique, which is called MSN in proximity (MSNP). 11,12 A mobile user can utilize either his handheld device's Bluetooth or Wi-Fi interface to detect proximate mobile users' handheld devices an...

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Clustering algorithm in VANETs: A survey

Cited by 19 publications

References 19 publications

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

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

Performance analysis of clustering for message classification and congestion control in DSRC/WAVE-based vehicular ad-hoc networks

K‐MING: A mobile proxy handoff control scheme for proximate group‐based geodata sharing

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