AI-based fog and edge computing: A systematic review, taxonomy and future directions
Resource management in computing is a very challenging problem that involves making
sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse …
sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse …
Scheduling IoT applications in edge and fog computing environments: A taxonomy and future directions
Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the
network with low latency and high-access bandwidth to support a diverse range of IoT …
network with low latency and high-access bandwidth to support a diverse range of IoT …
MADDPG-based joint service placement and task offloading in MEC empowered air-ground integrated networks
Multiaccess edge computing (MEC) empowered air–ground integrated networks (AGINs)
hold great promise in delivering accessible computing services for users and Internet of …
hold great promise in delivering accessible computing services for users and Internet of …
Offloading mechanisms based on reinforcement learning and deep learning algorithms in the fog computing environment
DH Abdulazeez, SK Askar - Ieee Access, 2023 - ieeexplore.ieee.org
Fog computing has emerged as a computing paradigm for resource-restricted Internet of
things (IoT) devices to support time-sensitive and computationally intensive applications …
things (IoT) devices to support time-sensitive and computationally intensive applications …
Deep reinforcement learning-based joint task and energy offloading in UAV-aided 6G intelligent edge networks
Edge networks are expected to play an important role in 6G where machine learning-based
methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile …
methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile …
[HTML][HTML] AI augmented Edge and Fog computing: Trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
Computing power network: A survey
S Yukun, L Bo, L Juniin, H Haonan, Z Xing… - China …, 2024 - ieeexplore.ieee.org
With the rapid development of cloud computing, edge computing, and smart devices,
computing power resources indicate a trend of ubiquitous deployment. The traditional …
computing power resources indicate a trend of ubiquitous deployment. The traditional …
Deep-reinforcement-learning-based distributed computation offloading in vehicular edge computing networks
L Geng, H Zhao, J Wang, A Kaushik… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Vehicular edge computing has emerged as a promising paradigm by offloading computation-
intensive latency-sensitive tasks to mobile-edge computing (MEC) servers. However, it is …
intensive latency-sensitive tasks to mobile-edge computing (MEC) servers. However, it is …
Digital-twin-assisted resource allocation for network slicing in industry 4.0 and beyond using distributed deep reinforcement learning
L Tang, Y Du, Q Liu, J Li, S Li… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Personalization is one of the primary emerging trends in Industry 4.0 and Beyond. Highly
personalized services will present a significant challenge to the existing algorithms for …
personalized services will present a significant challenge to the existing algorithms for …
Joint dynamic spectrum allocation for URLLC and eMBB in 6 G networks
6 G is a further evolution and development of 5 G to meet the need of various emerging
applications of multifarious user equipment (UE). Ultra-reliable and low-latency …
applications of multifarious user equipment (UE). Ultra-reliable and low-latency …