AI-empowered fog/edge resource management for IoT applications: A comprehensive review, research challenges and future perspectives

GK Walia, M Kumar, SS Gill - IEEE Communications Surveys & …, 2023 - ieeexplore.ieee.org
The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several
domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to …

AI-based fog and edge computing: A systematic review, taxonomy and future directions

S Iftikhar, SS Gill, C Song, M Xu, MS Aslanpour… - Internet of Things, 2023 - Elsevier
Resource management in computing is a very challenging problem that involves making
sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse …

DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks

D Yang, W Zhang, Q Ye, C Zhang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …

From 5G to 6G—challenges, technologies, and applications

AI Salameh, M El Tarhuni - Future Internet, 2022 - mdpi.com
As the deployment of 5G mobile radio networks gains momentum across the globe, the
wireless research community is already planning the successor of 5G. In this paper, we …

Future outlook on 6G technology for renewable energy sources (RES)

KY Yap, HH Chin, JJ Klemeš - Renewable and Sustainable Energy …, 2022 - Elsevier
As the renewable energy sources (RES) continues to grow in power system due to
emissions decarbonisation and sustainability policies, a fast and reliable connection …

Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges

M Al-Quraan, L Mohjazi, L Bariah… - … on Emerging Topics …, 2023 - ieeexplore.ieee.org
New technological advancements in wireless networks have enlarged the number of
connected devices. The unprecedented surge of data volume in wireless systems …

The metaverse: Survey, trends, novel pipeline ecosystem & future directions

H Sami, A Hammoud, M Arafeh… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The Metaverse offers a second world beyond reality, where boundaries are non-existent,
and possibilities are endless through engagement and immersive experiences using the …

[HTML][HTML] Deep learning for intelligent demand response and smart grids: A comprehensive survey

P Boopathy, M Liyanage, N Deepa, M Velavali… - Computer Science …, 2024 - Elsevier
Electricity is one of the mandatory commodities for mankind today. To address challenges
and issues in the transmission of electricity through the traditional grid, the concepts of smart …

Reinforcement learning-empowered mobile edge computing for 6G edge intelligence

P Wei, K Guo, Y Li, J Wang, W Feng, S **, N Ge… - Ieee …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive
and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its …

A reinforcement learning model for the reliability of blockchain oracles

M Taghavi, J Bentahar, H Otrok, K Bakhtiyari - Expert Systems with …, 2023 - Elsevier
Smart contracts struggle with the major limitation of operating on data that is solely residing
on the blockchain network. The need of recruiting third parties, known as oracles, to assist …