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

Khola Nazar; Yousaf Saeed; Abid Ali; Abeer D Algarni; Naglaa F Soliman; Abdelhamied A Ateya; Mohammed Saleh Ali Muthanna; Faisal Jamil

doi:10.3390/s22239157

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

Sensors (Basel). 2022 Nov 25;22(23):9157. doi: 10.3390/s22239157.

Authors

Khola Nazar¹, Yousaf Saeed¹, Abid Ali^{2

3}, Abeer D Algarni⁴, Naglaa F Soliman⁴, Abdelhamied A Ateya⁵, Mohammed Saleh Ali Muthanna⁶, Faisal Jamil⁷

Affiliations

¹ Department of ITT, The University of Haripur, Haripur 22620, Pakistan.
² Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan.
³ Department of Computer Science, Government Akhtar Nawaz Khan (Shaheed) Degree College KTS, Haripur 22620, Pakistan.
⁴ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁵ Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt.
⁶ Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia.
⁷ Department 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, Norway.

Abstract

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.

Keywords: VANET; congestion control; content pre-caching; machine learning; zone.

MeSH terms

Algorithms
Awareness
Disasters*
Intelligence*
Machine Learning

Grants and funding

This research was funded by the Norwegian University of Science and Technology and Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.