Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions
MY Akhlaqi, ZBM Hanapi - Journal of Network and Computer Applications, 2023 - Elsevier
Many enterprise companies migrate their services and applications to the cloud to benefit
from cloud computing advantages. Meanwhile, the rapidly increasing number of connected …
from cloud computing advantages. Meanwhile, the rapidly increasing number of connected …
Integration of multi access edge computing with unmanned aerial vehicles: Current techniques, open issues and research directions
During the last decade, research and development in the field of multi access edge
computing (MEC) has rapidly risen to prominence. One of the factors propelling MEC's …
computing (MEC) has rapidly risen to prominence. One of the factors propelling MEC's …
Computational intelligence and deep learning for next-generation edge-enabled industrial IoT
In this paper, we investigate how to deploy computational intelligence and deep learning
(DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can …
(DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can …
Semi-distributed resource management in UAV-aided MEC systems: A multi-agent federated reinforcement learning approach
Recently, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) has
been introduced as a promising edge paradigm for the future space-aerial-terrestrial …
been introduced as a promising edge paradigm for the future space-aerial-terrestrial …
URLLC edge networks with joint optimal user association, task offloading and resource allocation: A digital twin approach
D Van Huynh, VD Nguyen… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
This paper addresses the problem of minimising latency in computation offloading with
digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment …
digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment …
Outdated access point selection for mobile edge computing with cochannel interference
In this paper, we investigate a mobile edge computing (MEC) network, where the user has
some computational tasks to be assisted by multiple computational access points (CAPs) …
some computational tasks to be assisted by multiple computational access points (CAPs) …
Efficient and flexible management for industrial internet of things: A federated learning approach
In this paper, we devise an efficient and flexible management for mobile edge computing
(MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the …
(MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the …
Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks
S Tang, W Zhou, L Chen, L Lai, J Xia, L Fan - Physical Communication, 2021 - Elsevier
In this paper, we investigate how to optimize the federated edge learning (FEEL) in
unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) for B5G/6G networks …
unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) for B5G/6G networks …
Deep reinforcement learning based mobile edge computing for intelligent Internet of Things
R Zhao, X Wang, J Xia, L Fan - Physical Communication, 2020 - Elsevier
In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet
of things (IoT), where multiple users have some computational tasks assisted by multiple …
of things (IoT), where multiple users have some computational tasks assisted by multiple …
Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT
In this paper, we propose an ultra-reliable multiuser multiple-input multiple-output (MU-
MIMO) detector based on deep learning for the fifth-generation and beyond the fifth …
MIMO) detector based on deep learning for the fifth-generation and beyond the fifth …