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 …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Opportunities of artificial intelligence and machine learning in the food industry
The food processing and handling industry is the most significant business among the
various manufacturing industries in the entire world that subsidize the highest employability …
various manufacturing industries in the entire world that subsidize the highest employability …
A Novel Offloading Mechanism Leveraging Fuzzy Logic and Deep Reinforcement Learning to Improve IoT Application Performance in a Three-Layer Architecture …
DH Abdulazeez, SK Askar - IEEE Access, 2024 - ieeexplore.ieee.org
This paper presents a novel offloading technique designed to enhance the efficiency of
Internet of Things (IoT) applications within a sophisticated three-layer architecture situated in …
Internet of Things (IoT) applications within a sophisticated three-layer architecture situated in …
Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Performance analysis of deep reinforcement learning-based intelligent cooperative jamming method confronting multi-functional networked radar
W Zhang, T Zhao, Z Zhao, D Ma, F Liu - Signal Processing, 2023 - Elsevier
With the development of artificial intelligence technology, more and more intelligent
countermeasure methods are applied in military confrontation fields to improve the …
countermeasure methods are applied in military confrontation fields to improve the …
Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems
L Yin, Y Li - Applied Energy, 2022 - Elsevier
With the integration of renewable energy, pumped storage, and new energy storage into
multi-area integrated energy systems, the generation control of multi-area integrated energy …
multi-area integrated energy systems, the generation control of multi-area integrated energy …
The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey
N Abdi, A Albaseer, M Abdallah - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
As smart grids (SGs) increasingly rely on advanced technologies like sensors and
communication systems for efficient energy generation, distribution, and consumption, they …
communication systems for efficient energy generation, distribution, and consumption, they …
Healthcare: A priority-based energy harvesting scheme for managing sensor nodes in WBANs
S Gherairi - Ad Hoc Networks, 2022 - Elsevier
The eHealth service has been considered a potential resource issue for industry and
academia and is remarkably similar to a promising technology for continuous monitoring of …
academia and is remarkably similar to a promising technology for continuous monitoring of …
Switching-aware multi-agent deep reinforcement learning for target interception
D Fan, H Shen, L Dong - Applied Intelligence, 2023 - Springer
This paper investigates the multi-agent interception problem under switching topology
based on deep reinforcement learning. Due to communication restrictions or network …
based on deep reinforcement learning. Due to communication restrictions or network …