A survey on integrating edge computing with ai and blockchain in maritime domain, aerial systems, iot, and industry 4.0

A Alanhdi, L Toka - Ieee Access, 2024‏ - ieeexplore.ieee.org
In terms of digital transformation, organizations today are aware of the critical role that data
and information play in their expansion and development in light of the Internet of Things. To …

[HTML][HTML] Research trends in the use of machine learning applied in mobile networks: a bibliometric approach and research agenda

V García-Pineda, A Valencia-Arias, JC Patiño-Vanegas… - Informatics, 2023‏ - mdpi.com
This article aims to examine the research trends in the development of mobile networks from
machine learning. The methodological approach starts from an analysis of 260 academic …

Mobility-aware seamless virtual function migration in deviceless edge computing environments

Y Huang, Z Lin, T Yao, C Mo, X Shang… - IEEE Transactions …, 2023‏ - ieeexplore.ieee.org
Serverless Computing and Function-as-a-Service (FaaS) offer convenient and transparent
services to developers and users. The deployment and resource allocation of services are …

Effect of Hardware Imperfections and Energy Scavenging Nonlinearity on Overlay Networks in Shadowed Fading

T Le-Thanh, K Ho-Van - Arabian Journal for Science and Engineering, 2022‏ - Springer
In overlay networks, the cognitive sender (CS) assists the primary transmitter (PT) by
broadcasting the superposed signal composed of both cognitive and primary information …

Overlay networks with nonlinear energy scavenging and NOMA-assisted decoding: Security performance analysis

T Le-Thanh, K Ho-Van - Arabian Journal for Science and Engineering, 2022‏ - Springer
Overlay networks allow the unlicensed transmitter (UT) to aid the licensed transmitter (LT) by
sending the superimposed signal including messages of both UT and LT with a higher …

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks

Y Qiao, A Adhikary, K Kim, EN Huh, Z Han… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Federated learning (FL) is a distributed training technology that enhances data privacy in
mobile edge networks by allowing data owners to collaborate without transmitting raw data …

Efficient Service Function Chain Placement Over Heterogeneous Devices in Deviceless Edge Computing Environments

Y Huang, T Yao, Z Lin, X Shang, Y Yuan… - IEEE Transactions …, 2024‏ - ieeexplore.ieee.org
Heterogeneous devices in edge computing bring challenges as well as opportunities for
edge computing to utilize powerful and heterogeneous hardware for a variety of complex …

A novel non-volatile memory update mechanism for 6g edge computing

W Chen, D Li, Y Zhong, Y Tang - IEEE Transactions on Network …, 2022‏ - ieeexplore.ieee.org
In the 6G era, the service demand for extreme performance of artificial intelligence (AI)
applications poses a huge performance challenge for edge computing servers. The …

Nvm-enhanced mli placement for revenue maximization in uav-fog assisted mec with stable matching

A Kumar, S Kumari, A Pratap, S Kumar - Proceedings of the 25th …, 2024‏ - dl.acm.org
The rise of smart edge devices and the growing demand for advanced technologies such as
Machine Learning (ML) necessitate an evolution beyond 5G networks. Placing Machine …