[HTML][HTML] A comprehensive survey on reinforcement-learning-based computation offloading techniques in edge computing systems
In recent years, the number of embedded computing devices connected to the Internet has
exponentially increased. At the same time, new applications are becoming more complex …
exponentially increased. At the same time, new applications are becoming more complex …
Computation offloading method using stochastic games for software-defined-network-based multiagent mobile edge computing
G Wu, H Wang, H Zhang, Y Zhao… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to
process massive amounts of data. These computing tasks sometimes far exceed the …
process massive amounts of data. These computing tasks sometimes far exceed the …
Com-DDPG: Task offloading based on multiagent reinforcement learning for information-communication-enhanced mobile edge computing in the internet of vehicles
H Gao, X Wang, W Wei, A Al-Dulaimi… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The emergence of the Internet of Vehicles (IoV) introduces challenges regarding
computation-intensive and time-sensitive related services for data processing and …
computation-intensive and time-sensitive related services for data processing and …
Real-time performance of industrial IoT communication technologies: A review
With the growing need for automation and the ongoing merge of OT and IT, industrial
networks have to transport a high amount of heterogeneous data with mixed criticality, such …
networks have to transport a high amount of heterogeneous data with mixed criticality, such …
[HTML][HTML] Industrial Internet of Things enabled technologies, challenges, and future directions
Abstract The Industrial Internet of Things (IIoT) is recognized as the fourth industrial
revolution as it enhances productivity, dependability, and competitive performance by …
revolution as it enhances productivity, dependability, and competitive performance by …
Federated reinforcement learning-based resource allocation for D2D-aided digital twin edge networks in 6G industrial IoT
The sixth generation (6G) is conceived to address the expected high level of requirements
(such as ultra-high-data-transmission rate, support for the highest moving speed and …
(such as ultra-high-data-transmission rate, support for the highest moving speed and …
Intelligent task offloading and resource allocation in digital twin based aerial computing networks
To meet the future demands for ubiquitous communication coverage and
temporary/unexpected computing resources, aerial computing networks have been …
temporary/unexpected computing resources, aerial computing networks have been …
Energy efficient joint computation offloading and service caching for mobile edge computing: A deep reinforcement learning approach
Mobile Edge Computing (MEC) meets the delay requirements of emerging applications and
reduces energy consumption by pushing cloud functions to the edge of the networks …
reduces energy consumption by pushing cloud functions to the edge of the networks …
Deep-reinforcement-learning-based resource allocation for cloud gaming via edge computing
X Deng, J Zhang, H Zhang… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Compared with cloud computing, edge computing is capable of effectively solving the high
latency problem in cloud gaming. However, there are still several challenges to address for …
latency problem in cloud gaming. However, there are still several challenges to address for …
MEC-based dynamic controller placement in SD-IoV: A deep reinforcement learning approach
The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to
provide reliable and scalable wireless network services for the emerging applications in the …
provide reliable and scalable wireless network services for the emerging applications in the …