Computation offloading and content caching and delivery in Vehicular Edge Network: a survey
The past decade has witnessed the widespread adoption of Cloud Computing (CC) across automotive industries for a myriad of vehicular applications. A vehicular network that solely relies on CC, however, is susceptible to end-to-end latency due to the round-trip between data sources and cloud servers....
Saved in:
Main Authors: | , , , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/160420 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-160420 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1604202022-07-22T00:45:12Z Computation offloading and content caching and delivery in Vehicular Edge Network: a survey Dziyauddin, Rudzidatul Akmam Niyato, Dusit Nguyen Cong Luong Atan, Ahmad Ariff Aizuddin Mohd Izhar, Mohd Azri Mohd Azmi, Marwan Hadri Daud, Salwani Mohd School of Computer Science and Engineering Engineering::Computer science and engineering Vehicular Edge Network Offloading The past decade has witnessed the widespread adoption of Cloud Computing (CC) across automotive industries for a myriad of vehicular applications. A vehicular network that solely relies on CC, however, is susceptible to end-to-end latency due to the round-trip between data sources and cloud servers. Alternatively, the computing capability has been considered at the edge of vehicular network to achieve real-time analytics. Despite that, such consideration poses new questions on how data is offloaded and cached among the edge nodes and Autonomous Vehicles (AVs) in the environment of Vehicular Edge Network (VEN). In this paper, we outlined the aspects of VEN, particularly Vehicular Edge Computing (VEC), together with its architecture, layers, communications, and applications that are involved in the computation offloading (ComOf) and content caching and delivery (CachDel) scenarios. We extensively reviewed the existing approaches in solving ComOf and CachDel problems for the respective VEC architecture. The security aspect in ComOf and CachDel were critically discussed as well in the paper. Finally, we highlighted some key challenges, open issues, and future works of ComOf and CachDel in VEC. We would like to express our gratitude to Ministry of Higher Edu-cation Malaysia for funding the project under Fundamental Research Grant Scheme (FRGS) with the cost center number is R.K130000. 7856.5F277. 2022-07-22T00:45:12Z 2022-07-22T00:45:12Z 2021 Journal Article Dziyauddin, R. A., Niyato, D., Nguyen Cong Luong, Atan, A. A. A. M., Izhar, M. A. M., Azmi, M. H. & Daud, S. M. (2021). Computation offloading and content caching and delivery in Vehicular Edge Network: a survey. Computer Networks, 197, 108228-. https://dx.doi.org/10.1016/j.comnet.2021.108228 1389-1286 https://hdl.handle.net/10356/160420 10.1016/j.comnet.2021.108228 2-s2.0-85110211268 197 108228 en Computer Networks © 2021 Elsevier B.V. All rights reserved. |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Computer science and engineering Vehicular Edge Network Offloading |
spellingShingle |
Engineering::Computer science and engineering Vehicular Edge Network Offloading Dziyauddin, Rudzidatul Akmam Niyato, Dusit Nguyen Cong Luong Atan, Ahmad Ariff Aizuddin Mohd Izhar, Mohd Azri Mohd Azmi, Marwan Hadri Daud, Salwani Mohd Computation offloading and content caching and delivery in Vehicular Edge Network: a survey |
description |
The past decade has witnessed the widespread adoption of Cloud Computing (CC) across automotive industries for a myriad of vehicular applications. A vehicular network that solely relies on CC, however, is susceptible to end-to-end latency due to the round-trip between data sources and cloud servers. Alternatively, the computing capability has been considered at the edge of vehicular network to achieve real-time analytics. Despite that, such consideration poses new questions on how data is offloaded and cached among the edge nodes and Autonomous Vehicles (AVs) in the environment of Vehicular Edge Network (VEN). In this paper, we outlined the aspects of VEN, particularly Vehicular Edge Computing (VEC), together with its architecture, layers, communications, and applications that are involved in the computation offloading (ComOf) and content caching and delivery (CachDel) scenarios. We extensively reviewed the existing approaches in solving ComOf and CachDel problems for the respective VEC architecture. The security aspect in ComOf and CachDel were critically discussed as well in the paper. Finally, we highlighted some key challenges, open issues, and future works of ComOf and CachDel in VEC. |
author2 |
School of Computer Science and Engineering |
author_facet |
School of Computer Science and Engineering Dziyauddin, Rudzidatul Akmam Niyato, Dusit Nguyen Cong Luong Atan, Ahmad Ariff Aizuddin Mohd Izhar, Mohd Azri Mohd Azmi, Marwan Hadri Daud, Salwani Mohd |
format |
Article |
author |
Dziyauddin, Rudzidatul Akmam Niyato, Dusit Nguyen Cong Luong Atan, Ahmad Ariff Aizuddin Mohd Izhar, Mohd Azri Mohd Azmi, Marwan Hadri Daud, Salwani Mohd |
author_sort |
Dziyauddin, Rudzidatul Akmam |
title |
Computation offloading and content caching and delivery in Vehicular Edge Network: a survey |
title_short |
Computation offloading and content caching and delivery in Vehicular Edge Network: a survey |
title_full |
Computation offloading and content caching and delivery in Vehicular Edge Network: a survey |
title_fullStr |
Computation offloading and content caching and delivery in Vehicular Edge Network: a survey |
title_full_unstemmed |
Computation offloading and content caching and delivery in Vehicular Edge Network: a survey |
title_sort |
computation offloading and content caching and delivery in vehicular edge network: a survey |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/160420 |
_version_ |
1739837464156045312 |