scholarly journals Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges

2017 ◽  
Vol 55 (4) ◽  
pp. 54-61 ◽  
Author(s):  
Tuyen X. Tran ◽  
Abolfazl Hajisami ◽  
Parul Pandey ◽  
Dario Pompili
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
5G Networks

scholarly journals Deep Learning at the Mobile Edge: Opportunities for 5G Networks

2020 ◽  
Vol 10 (14) ◽  
pp. 4735 ◽  
Author(s):  
Miranda McClellan ◽  
Cristina Cervelló-Pastor ◽  
Sebastià Sallent
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Connected Vehicles ◽  
Mobile Edge Computing ◽  
5G Networks ◽  
End User ◽  
Network Resources ◽  
Mobility Modeling ◽  
Novel Applications ◽  
Adaptive Resource

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.

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