A comprehensive survey on training acceleration for large machine learning models in IoT
The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …
Multi-agent reinforcement learning-based distributed channel access for next generation wireless networks
Z Guo, Z Chen, P Liu, J Luo, X Yang… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
In the next generation wireless networks, more applications will emerge, covering virtual
reality movies, augmented reality, holographic three-dimensional telepresence, haptic …
reality movies, augmented reality, holographic three-dimensional telepresence, haptic …
Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges
Internet of Things (IoT) devices are increasingly popular due to their wide array of
application domains. In IoT networks, sensor nodes are often connected in the form of a …
application domains. In IoT networks, sensor nodes are often connected in the form of a …
Cybertwin-driven DRL-based adaptive transmission scheduling for software defined vehicular networks
Efficient transmission control is a challenging issue in vehicular networks due to the highly
dynamic and unpredictable link status. In this paper, we propose a cybertwin-driven learning …
dynamic and unpredictable link status. In this paper, we propose a cybertwin-driven learning …
Deep reinforcement learning for optimizing RIS-assisted HD-FD wireless systems
This letter investigates the reconfigurable intelligent surface (RIS)-assisted multiple-input
single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) …
single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) …
Optimizing aoi in uav-ris assisted iot networks: Off policy vs. on policy
In urban environments, tall buildings or structures can pose limits on the direct channel link
between a base station (BS) and an Internet of Thing device (IoTD) for wireless …
between a base station (BS) and an Internet of Thing device (IoTD) for wireless …
Reinforcement learning framework for server placement and workload allocation in multiaccess edge computing
A Mazloomi, H Sami, J Bentahar… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Cloud computing is a reliable solution to provide distributed computation power. However,
real-time response is still challenging regarding the enormous amount of data generated by …
real-time response is still challenging regarding the enormous amount of data generated by …
Decentralized configuration of TSCH-based IoT networks for distinctive QoS: A deep reinforcement learning approach
H Hajizadeh, M Nabi… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
The IEEE 802.15. 4 time-slotted channel hopping (TSCH) is widely used as a reliable, low-
power, and low-cost communication technology for many industrial Internet of Things (IoT) …
power, and low-cost communication technology for many industrial Internet of Things (IoT) …
VC-PPQ: privacy-preserving Q-learning based video caching optimization in mobile edge networks
Mobile edge caching has great advantages in alleviating video traffic pressure and reducing
transmission delay, which is considered as a hopeful solution to improve video resource …
transmission delay, which is considered as a hopeful solution to improve video resource …
Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications
Abstract The Industrial Internet of Things (IIoT) is an emerging and promising concept that
allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of …
allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of …