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Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system...

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Autores principales: Nazar, Khola, Saeed, Yousaf, Ali, Abid, Algarni, Abeer D., Soliman, Naglaa F., Ateya, Abdelhamied A., Muthanna, Mohammed Saleh Ali, Jamil, Faisal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737187/
https://www.ncbi.nlm.nih.gov/pubmed/36501861
http://dx.doi.org/10.3390/s22239157
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author Nazar, Khola
Saeed, Yousaf
Ali, Abid
Algarni, Abeer D.
Soliman, Naglaa F.
Ateya, Abdelhamied A.
Muthanna, Mohammed Saleh Ali
Jamil, Faisal
author_facet Nazar, Khola
Saeed, Yousaf
Ali, Abid
Algarni, Abeer D.
Soliman, Naglaa F.
Ateya, Abdelhamied A.
Muthanna, Mohammed Saleh Ali
Jamil, Faisal
author_sort Nazar, Khola
collection PubMed
description In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content’s early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.
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spelling pubmed-97371872022-12-11 Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control Nazar, Khola Saeed, Yousaf Ali, Abid Algarni, Abeer D. Soliman, Naglaa F. Ateya, Abdelhamied A. Muthanna, Mohammed Saleh Ali Jamil, Faisal Sensors (Basel) Article In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content’s early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion. MDPI 2022-11-25 /pmc/articles/PMC9737187/ /pubmed/36501861 http://dx.doi.org/10.3390/s22239157 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nazar, Khola
Saeed, Yousaf
Ali, Abid
Algarni, Abeer D.
Soliman, Naglaa F.
Ateya, Abdelhamied A.
Muthanna, Mohammed Saleh Ali
Jamil, Faisal
Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
title Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
title_full Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
title_fullStr Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
title_full_unstemmed Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
title_short Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
title_sort towards intelligent zone-based content pre-caching approach in vanet for congestion control
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737187/
https://www.ncbi.nlm.nih.gov/pubmed/36501861
http://dx.doi.org/10.3390/s22239157
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