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Dynamic Vehicular Clustering Enhancing Video on Demand Services Over Vehicular Ad-hoc Networks

M. Almutiq1, L. Sellami1,2,*, B. Alaya1,3

1 Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, 6633, Buraidah, 51452, Saudi Arabia
2 CONPRI Laboratory, University of Gabes, Tunisia
3 IResCoMath Laboratory, University of Gabes, Tunisia

* Corresponding Authors: L. Sellami. Email: ,

Computers, Materials & Continua 2022, 72(2), 3493-3510.


Nowadays, video streaming applications are becoming one of the tendencies driving vehicular network users. In this work, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different vehicles included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over vehicular ad-hoc networks (VANET). The proposed algorithm takes advantage of the small cells concept and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)-Advanced network. Vehicles are clustered together to form dynamically ad-hoc sub-networks included in the vehicular network. The goal of our clustering algorithm is to take into account several characteristics, such as the vehicle's position and acceleration to reduce latency and packet loss. Therefore, each cluster is counted as a small cell containing vehicular nodes and an access point that is elected regarding some particular specifications. Based on the exceptional features of the LTE-Advanced network (small cells and wireless backhauls) the DVC algorithm is a promising scheme for video streaming services over VANET systems. Experiments were carried out with a virtual topology of the VANET network created with four clusters to implement the DVC algorithm. The results were compared with other algorithms such as Virtual Trust-ability Data transmission (VTD), Named Data Networking (NDN), and Socially Aware Security Message Forwarding (SASMF). Our algorithm can effectively improve the transmission rate of data packets at the expense of a slight increase in end-to-end delay and control overhead.


Cite This Article

M. Almutiq, L. Sellami and B. Alaya, "Dynamic vehicular clustering enhancing video on demand services over vehicular ad-hoc networks," Computers, Materials & Continua, vol. 72, no.2, pp. 3493–3510, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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