Machine learning for service migration: a survey

N Toumi, M Bagaa, A Ksentini - IEEE Communications Surveys …, 2023 - ieeexplore.ieee.org
Future communication networks are envisioned to satisfy increasingly granular and dynamic
requirements to accommodate the application and user demands. Indeed, novel immersive …

SDN-based service mobility management in MEC-enabled 5G and beyond vehicular networks

SDA Shah, MA Gregory, S Li… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
The next-generation mobile cellular networks are dedicated to providing a valued and
unique service experience by supporting ultrareliable and low-latency communication …

Digital twin-assisted and mobility-aware service migration in mobile edge computing

E Bozkaya - Computer Networks, 2023 - Elsevier
Abstract Mobile Edge Computing (MEC) is emerging as one of the key technologies to
process massive amount of data at the edge of the network for upcoming 6G networks. In the …

Deep deterministic policy gradient to minimize the age of information in cellular V2X communications

Z Mlika, S Cherkaoui - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
This paper studies the problem of minimizing the age of information (AoI) in cellular vehicle-
to-everything communications. To provide minimal AoI and high reliability for vehicles' safety …

Ultra-reliable low-latency slicing in space-air-ground multi-access edge computing networks for next-generation internet of things and mobile applications

A Asheralieva, D Niyato, X Wei - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
We study the problem of ultrareliable and low-latency slicing in multiaccess edge computing
(MEC) systems for the next-generation Internet of Things (IoT) and mobile applications …

Massive IoT access with NOMA in 5G networks and beyond using online competitiveness and learning

Z Mlika, S Cherkaoui - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
This article studies the problem of online user grouping, scheduling, and power allocation
for massive Internet of Things (IoT) access in beyond 5G networks using nonorthogonal …

Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

Z Mlika, S Cherkaoui - Annals of Telecommunications, 2021 - Springer
This paper studies the multi-agent resource allocation problem in vehicular networks using
non-orthogonal multiple access (NOMA) and network slicing. Vehicles want to broadcast …

Efficient dynamic distributed resource slicing in 6g multi-access edge computing networks with online admm and message passing graph neural networks

A Asheralieva, D Niyato… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
We consider the problem of resource slicing in the 6 th generation multi-access edge
computing (6G-MEC) network. The network includes many non-stationary space-air-ground …

Geoss: geographic segmentation security barriers for virtual emotion detection with discriminative priorities in intelligent cooperative vehicular system

S Lee, S Lee, Y Choi, J Ben-Othman… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
A development of the integrated vehicular system through ground and aerial cooperation
using intelligent mobile robots and smart UAVs is required to support various applications …

Federated-learning-based energy-efficient load balancing for UAV-enabled MEC system in vehicular networks

A Shin, Y Lim - Energies, 2023 - mdpi.com
At present, with the intelligence that has been achieved in computer and communication
technologies, vehicles can provide many convenient functions to users. However, it is …