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....

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Main Authors: Dziyauddin, Rudzidatul Akmam, Niyato, Dusit, Nguyen Cong Luong, Atan, Ahmad Ariff Aizuddin Mohd, Izhar, Mohd Azri Mohd, Azmi, Marwan Hadri, Daud, Salwani Mohd
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160420
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Institution: Nanyang Technological University
Language: English
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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