Deep reinforcement learning for online resource allocation in IoT networks: Technology, development, and future challenges
The growing number of complex and heterogeneous Internet of Things (IoT) applications
has imposed a high demand for scarce communications and computing resources. To meet …
has imposed a high demand for scarce communications and computing resources. To meet …
DeepEdge: A new QoE-based resource allocation framework using deep reinforcement learning for future heterogeneous edge-IoT applications
Edge computing is emerging to empower the future of Internet of Things (IoT) applications.
However, due to heterogeneity of applications, it is a significant challenge for the edge cloud …
However, due to heterogeneity of applications, it is a significant challenge for the edge cloud …
Resource allocation based on deep reinforcement learning in IoT edge computing
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
FLIRRAS: fast learning with integrated reward and reduced action space for online multitask offloading
M Ma, C Gong, L Wu, Y Yang - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
With the rapid development of edge data intelligence, task offloading (TO) and resource
allocation (RA) optimization in multiaccess edge computing networks can significantly …
allocation (RA) optimization in multiaccess edge computing networks can significantly …
iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …
shown amazing performance in solving complex problems to support the Internet of Things …
Jointly optimizing resource and heterogeneity in IoT networks using a Three-Stage Asynchronous Federated Reinforcement Learning
ASMS Sagar, Y Chen, MA Rob, HS Kim - Internet of Things, 2024 - Elsevier
The rapid expansion of Internet of Things (IoT) networks underscores the demand for
efficient and secure machine learning methods suited for geographically dispersed …
efficient and secure machine learning methods suited for geographically dispersed …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Deep reinforcement learning based approach for online service placement and computation resource allocation in edge computing
Due to the urgent emergence of computation-intensive intelligent applications on end
devices, edge computing has been put forward as an extension of cloud computing, to …
devices, edge computing has been put forward as an extension of cloud computing, to …
DA-DRLS: Drift adaptive deep reinforcement learning based scheduling for IoT resource management
In order to fulfill the tremendous resource demand by diverse IoT applications, the large-
scale resource-constrained IoT ecosystem requires a robust resource management …
scale resource-constrained IoT ecosystem requires a robust resource management …
Multi-agent reinforcement learning for intelligent resource allocation in IIoT networks
J Rosenberger, M Urlaub… - 2021 IEEE Global …, 2021 - ieeexplore.ieee.org
In the industrial Internet of Things (IIoT), a high number of devices with limited resources, like
computational power, memory, bandwidth and, in case of wireless sensor networks, also …
computational power, memory, bandwidth and, in case of wireless sensor networks, also …
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