Abstract
Mobile edge computing enhances cloud computing capability to users at the edge of wireless networks, for future networks in particular. Network architecture defined by ETSI, has been developed for multi-access edge computing (MEC). This concept has been a hot topic for the latest network technologies in the last decade. Offloading techniques are part of this research as well. Consequently, many diverse offloading techniques for Mobile Edge Computing have been proposed in the literature. Some confusion might occur in relation to this development. Due to this reason, a simplification work is needed for different kinds of offloading techniques which will be used in future wireless networks under Mobile Edge Computing topic. In this paper, many diverse offloading techniques will be presented, and they will be discussed with the aim of providing a sufficient foundation for the upcoming technologies. This paper also provides the key methodologies for collaborative and intelligent methods to be used in future wireless networks. Finally, an outlook for the topic will be given for the future offloading techniques that are yet to come.
Original language | English |
---|---|
Title of host publication | International Conference on Smart Computing and Application, ICSCA 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350347050 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 International Conference on Smart Computing and Application, ICSCA 2023 - Hail, Saudi Arabia Duration: 5 Feb 2023 → 6 Feb 2023 |
Publication series
Name | International Conference on Smart Computing and Application, ICSCA 2023 |
---|
Conference
Conference | 2023 International Conference on Smart Computing and Application, ICSCA 2023 |
---|---|
Country/Territory | Saudi Arabia |
City | Hail |
Period | 5/02/23 → 6/02/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- 5G
- computational offloading
- IoT
- IoV
- machine learning
- Mobile edge computing
- multi-access edge computing
- task offloading