CoChat
Sign Up
Cited by 11Caching and Content DeliveryVehicular Ad Hoc Networks (VANETs)IoT and Edge/Fog Computing

The DOI matches its Crossref record.

A Review on AI-Enabled Content Caching in Vehicular Edge Caching and Networks

Masood, Arooj, Tuan, Do Quang, Lakew, Demeke Shumeye, Dao, Nhu-Ngoc, Cho, Sungrae·2023·2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
DOI 10.1109/icaiic57133.2023.10067094
Open this paper in CoChat — free account

Abstract

With the emergence of many vehicular networking applications, the amount of data traffic in vehicular networks (VNs) is increasing at an exponential rate. Vehicular edge caching (VEC), which utilizes the on board storage resources and road-side edge servers has been considered as a promising technology to satisfy the demands of vehicular networking applications in VNs. However, the classical optimization schemes for content caching perform poorly in VEC primarily owing to the characteristics of the VNs. For instance, the high mobility of vehicles makes the content popularity highly time-varying and location-dependent. Artificial intelligence (AI) and machine learning (ML) is considered as a powerful technique to improve the caching efficiency in VEC. In this paper, we discuss the distinct challenges in VEC and provide a review on AI-enabled content caching schemes in VEC networks. In addition, we highlight the existing challenges and open research issues in order to spur further investigation in this area.

Overview

Integrity signals

Registry

Crossref · 10.1109/icaiic57133.2023.10067094

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 17

  1. 10.1109/MNET.011.2000561 · DOI
  2. 10.1109/COMST.2020.3012961 · DOI
  3. Dziyauddin, 2019, Computation offloading and content caching delivery in vehicular edge computing: A survey, arXiv preprint
  4. 10.1109/ICC.2017.7997019 · DOI
  5. 10.1109/CSCN.2018.8581849 · DOI
  6. 10.1109/TWC.2020.3003339 · DOI
  7. 10.1109/TVT.2020.3004720 · DOI
  8. 10.1109/MWC.2019.1800411 · DOI
  9. 10.1109/JIOT.2019.2945640 · DOI
  10. 10.1109/TVT.2020.2973705 · DOI
  11. 10.1109/WCNC45663.2020.9120600 · DOI
  12. 10.1186/s13677-020-00182-x · DOI
  13. 10.1109/TITS.2020.3017474 · DOI · Paper page
  14. 10.3390/app122311942 · DOI
  15. 10.1109/TITS.2020.2997832 · DOI
  16. 10.1109/ICOIN.2019.8718113 · DOI
  17. 10.1109/TITS.2020.2976572 · DOI

Cited by

Sorted by citations

Metadata

Authors
Masood, A., Tuan, D. Q., Lakew, D. S., Dao, N.-N., & Cho, S.
Affiliations
Sejong University (KR)
ORCID
Venue
2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
Publisher
IEEE
Published
2023-02-20
Pages
713-717
DOI
10.1109/icaiic57133.2023.10067094
OpenAlex
W4360604906
Cited by
11
References
0
Citations by year
2026: 1 · 2025: 5 · 2024: 4 · 2023: 1
FWCI
3.32
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (publishedVersion)
Funders
National Research Foundation of Korea(NRF)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords
content caching, vehicular networks, artificial intelligence, machine learning, vehicle mobility

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review