Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160420
Title: Computation offloading and content caching and delivery in Vehicular Edge Network: a survey
Authors: Dziyauddin, Rudzidatul Akmam
Niyato, Dusit
Nguyen Cong Luong
Atan, Ahmad Ariff Aizuddin Mohd
Izhar, Mohd Azri Mohd
Azmi, Marwan Hadri
Daud, Salwani Mohd
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: 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
Journal: Computer Networks
Abstract: 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.
URI: https://hdl.handle.net/10356/160420
ISSN: 1389-1286
DOI: 10.1016/j.comnet.2021.108228
Schools: School of Computer Science and Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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