Evidence map›Paper›PMID 36501861›Full record

ArticleSensors (Basel, Switzerland)2022

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

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 6 institutions in 5 countries.

Khola NazarDepartment of ITT, The University of Haripur, Haripur 22620, Pakistan.
Yousaf SaeedDepartment of ITT, The University of Haripur, Haripur 22620, Pakistan.
Abid AliDepartment of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan.ORCID 0000-0003-2917-6598
Abeer D AlgarniDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Naglaa F SolimanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0001-7322-1857
Abdelhamied A AteyaDepartment of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt.ORCID 0000-0002-1610-9612
Mohammed Saleh Ali MuthannaInstitute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia.ORCID 0000-0002-1165-7812
Faisal JamilDepartment 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.
Princess Nourah bint Abdulrahman University · SAUniversity of Haripur · PKNorwegian University of Science and Technology · NOSouthern Federal University · RUUniversity of Engineering and Technology Taxila · PKZagazig University · EG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

DisastersIntelligenceAlgorithmsAwarenessMachine Learningcongestion controlcontent pre-cachingmachine learningVANETzone

Identifiers

PMID36501861
PMCPMC9737187
OpenAlexW4310057012

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.