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

Publication Type : Journal Article

Publisher : Inderscience Enterprises Ltd

Source : International Journal of Vehicle Information and Communication Systems, Inderscience Enterprises Ltd., Volume 4, Issue 1, p.55-77 (2019)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064645885&doi=10.1504%2fIJVICS.2019.099067&partnerID=40&md5=fa6fa21990ec34aee61fcf0d2d9f08ce

Keywords : Clustering, Dedicated short range communications, Ieee 1609, Ieee 802.11p, IEEE Standards, Intelligent systems, Intelligent vehicle highway systems, K-means, K-means clustering, Learning algorithms, Machine learning, Omnet, Partitioning around medoids, Pulse amplitude modulation, RInside, Silhouette plot, Traffic control, Vehicular ad hoc networks, Veins

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2019

Abstract : 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. © 2019 Inderscience Enterprises Ltd.

Cite this Research Publication : R. G. Reddy and Ramanathan, R., “Performance analysis of clustering for message classification and congestion control in DSRC/ WAVE-based vehicular ad-hoc networks”, International Journal of Vehicle Information and Communication Systems, vol. 4, no. 1, pp. 55-77, 2019.

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