AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope

New technology is needed to meet the latency and bandwidth issues present in cloud computing architecture specially to support the currency of 5G networks. Accordingly, mobile edge computing (MEC) came into picture as novel emerging solutions to overcome many cloud computing issues. In this contempo...

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Vydáno v:Arabian journal for science and engineering (2011) Ročník 47; číslo 8; s. 9801 - 9831
Hlavní autoři: Singh, Ashish, Satapathy, Suresh Chandra, Roy, Arnab, Gutub, Adnan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022
Springer Nature B.V
Témata:
Artificial intelligence
Bandwidths
Cell phones
Cloud computing
Computer architecture
Cybersecurity
Deep learning
Edge computing
Energy consumption
Engineering
Humanities and Social Sciences
Internet of Things
Machine learning
Mobile computing
multidisciplinary
Network latency
New technology
Prediction models
Privacy
Research Article-Computer Engineering and Computer Science
Science
Mobile edge computing (MEC)
Mobile communications
AI and FL technology
Security and privacy challenges
Edge technology
Internet of things (IoT)
ISSN:2193-567X, 1319-8025, 2191-4281
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Popis
Shrnutí:New technology is needed to meet the latency and bandwidth issues present in cloud computing architecture specially to support the currency of 5G networks. Accordingly, mobile edge computing (MEC) came into picture as novel emerging solutions to overcome many cloud computing issues. In this contemporary technology, the computation server and processing units are nearby edge servers to reduce latency, increase the network bandwidth and reduce energy consumption in user devices. These features can integrate with several domains such as the internet of things, artificial intelligence (AI), federated learning (FL) and fog computing, etc., to make the system more robust, elastic, efficient, and accurate. Regardless of the advantages, MEC faces several challenges, including security and privacy, deployment protocols, and offloading management. Although, various studies have been found tuning MEC to solve such challenges, the literature provide more ideas for smart developments toward applications particularly using FL and AI. Most researches miss combining interesting aspects of MEC, such as machine learning and deep learning approaches limiting works to only single aspect. Thus, a literature work is needed to focus on all the aspects of MEC together. This study aims to present a comprehensive survey on MEC by providing all necessary information, including network architecture, advantages, objectives, access technologies, deployment templates, characteristics, and many more. The work is not limited to only MEC background but also covers the AI and FL approaches used within MEC, allowing mobile phones to learn a shared predictive model collaboratively. This survey also provides information regarding security and privacy challenges as well as attacks on MEC and their solutions. The applications of MEC illustrate different sectors where MEC is applicable further highlighting open issues and challenges to be investigated.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-021-06348-2