A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
March 31, 2017 ยท Declared Dead ยท ๐ IEEE Access
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Shuo Wang, Xing Zhang, Yan Zhang, Lin Wang, Juwo Yang, Wenbo Wang
arXiv ID
1703.10750
Category
cs.NI: Networking & Internet
Citations
853
Venue
IEEE Access
Last Checked
1 month ago
Abstract
As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Networking & Internet
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
R.I.P.
๐ป
Ghosted
A Survey of Indoor Localization Systems and Technologies
R.I.P.
๐ป
Ghosted
Survey of Important Issues in UAV Communication Networks
R.I.P.
๐ป
Ghosted
Network Function Virtualization: State-of-the-art and Research Challenges
R.I.P.
๐ป
Ghosted
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted