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 …

Reinforcement learning methods for computation offloading: a systematic review

Z Zabihi, AM Eftekhari Moghadam… - ACM Computing …, 2023 - dl.acm.org
Today, cloud computation offloading may not be an appropriate solution for delay-sensitive
applications due to the long distance between end-devices and remote datacenters. In …

Task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach

Y Wu, J **a, C Gao, J Ou, C Fan, J Ou, D Fan - Physical Communication, 2022 - Elsevier
This article examines a multi-user mobile edge computing (MEC) system for the Internet of
Vehicle (IoV), where one edge point (EP) nearby the vehicles can help assist in processing …

Computation offloading and service caching for intelligent transportation systems with digital twin

X Xu, Z Liu, M Bilal, S Vimal… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the
increasing computation requirements of mobile applications. In MEC-enabled intelligent …

An integrated model of UTAUT2 to understand consumers' 5G technology acceptance using SEM-ANN approach

S Mustafa, W Zhang, S Anwar, K Jamil, S Rana - Scientific Reports, 2022 - nature.com
It has been a decade since the first extensive study on the internet's adoption and use was
conducted. Circumstances have changed in the last decade internet has become an …

A survey of mobile edge computing for the metaverse: Architectures, applications, and challenges

Y Wang, J Zhao - 2022 IEEE 8th International Conference on …, 2022 - ieeexplore.ieee.org
Metaverse is an emerging virtual universe where humans can have real-time interactions
and solid social links like in the physical world, and it opens up a new era of Internet and …

Cooperative task offloading and service caching for digital twin edge networks: A graph attention multi-agent reinforcement learning approach

Z Yao, S **a, Y Li, G Wu - IEEE Journal on Selected Areas in …, 2023 - ieeexplore.ieee.org
Mobile edge computing (MEC) enables various services to be cached in close proximity to
the user equipments (UEs), thereby reducing the service delay of many emerging …

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

M Khani, MM Sadr, S Jamali - Concurrency and Computation …, 2024 - Wiley Online Library
Network architects and engineers face challenges in meeting the increasing complexity and
low‐latency requirements of various services. To tackle these challenges, multi‐access …

Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading in industrial networks

S Chen, J Chen, Y Miao, Q Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the rapid development of mobile industrial applications and due to the limited coverage
of static edge servers, traditional edge computing technology has great limitations in …

Cloud–edge collaborative resource allocation for blockchain-enabled Internet of Things: A collective reinforcement learning approach

M Li, P Pei, FR Yu, P Si, Y Li, E Sun… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Driven by numerous emerging mobile devices and various Quality-of-Service (QoS)
requirements, mobile-edge computing (MEC) has been recognized as a prospective …