research-article

Payoff-based Dynamic Segment Replication and Graph Classification Method with Attribute Vectors Adapted to Urban VANET

Author:

Bechir AlayaAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 3

Article No.: 85, Pages 1 - 22

https://doi.org/10.1145/3440018

Published: 16 August 2021 Publication History

Abstract

Due to the number of constraints and the dynamic nature of vehicular ad hoc networks (VANET), effective video broadcasting always remains a difficult task. In this work, we proposed a quality of video visualization guarantee model based on a feedback loop and an efficient algorithm for segmenting and replicating video segments using the Payoff-based Dynamic Segment Replication Policy (P-DSR). In the urban VANET environment, P-DSR is defined by taking into account the position of the vehicles, the speed, the direction, the number of neighboring vehicles, and the reputation of each node to stabilize the urban VANET topology. However, the management of various load control parameters between the different components of the urban VANET network remains a problem to be studied. This work uses a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input. This algorithm aims to provide an improved class lifetime, an improved video segment delivery rate, a reduced inter-class overload, and an optimization of a global criterion. A scalable algorithm is used to optimize the parameters of the GCMAV. The simulations were carried out using the NetSim simulator and Multi-Objective Evolutionary Algorithms framework to optimize parameters. Experiments were carried out with realistic maps of Open Street Maps and its results were compared with other algorithms such as Seamless and Authorized Multimedia Streaming and P-DSR. The survey suggests that the proposed methodology works well concerning the average lifetime of the inter-classes and the delivery rate of video segments.

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Cited By

Zidi SAlaya BMoulahi TAl-Shargabi AKhediri S(2024)Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network EnvironmentIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33474845(132-145)Online publication date: 2024
https://doi.org/10.1109/OJITS.2023.3347484
Almutiq MSellami LAlaya B(2022)Dynamic Vehicular Clustering Enhancing Video on Demand Services Over Vehicular Ad-hoc NetworksComputers, Materials & Continua10.32604/cmc.2022.02457172:2(3493-3510)Online publication date: 2022
https://doi.org/10.32604/cmc.2022.024571
Alaya BSellami LAl Mutiq M(2022)Towards a Dynamic Vehicular Clustering Improving VoD Services on Vehicular Ad Hoc NetworksAdvances in Computational Collective Intelligence10.1007/978-3-031-16210-7_34(409-422)Online publication date: 21-Sep-2022
https://doi.org/10.1007/978-3-031-16210-7_34

Index Terms

Payoff-based Dynamic Segment Replication and Graph Classification Method with Attribute Vectors Adapted to Urban VANET
1. Human-centered computing

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 3

August 2021

443 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3476118

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 16 August 2021

Accepted: 01 November 2020

Revised: 01 November 2020

Received: 01 April 2020

Published in TOMM Volume 17, Issue 3

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Cited By

Zidi SAlaya BMoulahi TAl-Shargabi AKhediri S(2024)Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network EnvironmentIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33474845(132-145)Online publication date: 2024
https://doi.org/10.1109/OJITS.2023.3347484
Almutiq MSellami LAlaya B(2022)Dynamic Vehicular Clustering Enhancing Video on Demand Services Over Vehicular Ad-hoc NetworksComputers, Materials & Continua10.32604/cmc.2022.02457172:2(3493-3510)Online publication date: 2022
https://doi.org/10.32604/cmc.2022.024571
Alaya BSellami LAl Mutiq M(2022)Towards a Dynamic Vehicular Clustering Improving VoD Services on Vehicular Ad Hoc NetworksAdvances in Computational Collective Intelligence10.1007/978-3-031-16210-7_34(409-422)Online publication date: 21-Sep-2022
https://doi.org/10.1007/978-3-031-16210-7_34

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