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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9157 |
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| author | Khola Nazar Yousaf Saeed Abid Ali Abeer D. Algarni Naglaa F. Soliman Abdelhamied A. Ateya Mohammed Saleh Ali Muthanna Faisal Jamil |
| author_facet | Khola Nazar Yousaf Saeed Abid Ali Abeer D. Algarni Naglaa F. Soliman Abdelhamied A. Ateya Mohammed Saleh Ali Muthanna Faisal Jamil |
| author_sort | Khola Nazar |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T17:32:35Z |
| format | Article |
| id | doaj.art-23d31f9db0364b69820f4cf6b3fa5c64 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:35Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-23d31f9db0364b69820f4cf6b3fa5c642023-11-24T12:09:30ZengMDPI AGSensors1424-82202022-11-012223915710.3390/s22239157Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion ControlKhola Nazar0Yousaf Saeed1Abid Ali2Abeer D. Algarni3Naglaa F. Soliman4Abdelhamied A. Ateya5Mohammed Saleh Ali Muthanna6Faisal Jamil7Department of ITT, The University of Haripur, Haripur 22620, PakistanDepartment of ITT, The University of Haripur, Haripur 22620, PakistanDepartment of Computer Science, University of Engineering and Technology, Taxila 47050, PakistanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, EgyptInstitute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, RussiaDepartment of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (N.T.N.U.), Larsgårdsvegen 2, 6009 Ålesund, NorwayIn 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.https://www.mdpi.com/1424-8220/22/23/9157machine learningcontent pre-cachingzonecongestion controlVANET |
| spellingShingle | Khola Nazar Yousaf Saeed Abid Ali Abeer D. Algarni Naglaa F. Soliman Abdelhamied A. Ateya Mohammed Saleh Ali Muthanna Faisal Jamil Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control Sensors machine learning content pre-caching zone congestion control VANET |
| 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 | machine learning content pre-caching zone congestion control VANET |
| url | https://www.mdpi.com/1424-8220/22/23/9157 |
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