Deep-reinforcement-learning-based energy-efficient resource management for social and cognitive Internet of Things
Internet of Things (IoT) has attracted much interest due to its wide applications, such as
smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable …
smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable …
A new block-based reinforcement learning approach for distributed resource allocation in clustered IoT networks
Resource allocation and spectrum management are two major challenges in the massive
scale deployment of Internet of Things (IoT) and Machine-to-Machine (M2M) communication …
scale deployment of Internet of Things (IoT) and Machine-to-Machine (M2M) communication …
Deep reinforcement learning based algorithm for symbiotic radio iot throughput optimization in 6g network
GM Salama, SS Metwly, EG Shehata… - IEEE …, 2023 - ieeexplore.ieee.org
Internet of Things (IoT)-based 6G is expected to revolutionize our world. Various candidate
technologies have been proposed to meet IoT system requirements based on 6G, symbiotic …
technologies have been proposed to meet IoT system requirements based on 6G, symbiotic …
A GNN-based supervised learning framework for resource allocation in wireless IoT networks
The Internet of Things (IoT) allows physical devices to be connected over the wireless
networks. Although device-to-device (D2D) communication has emerged as a promising …
networks. Although device-to-device (D2D) communication has emerged as a promising …
Deep reinforcement learning optimal transmission algorithm for cognitive Internet of Things with RF energy harvesting
S Guo, X Zhao - IEEE Transactions on Cognitive …, 2022 - ieeexplore.ieee.org
Spectrum scarcity and energy limitation are becoming two critical issues in designing
Internet of Things (IoT). As two promising technologies, cognitive radio (CR) and radio …
Internet of Things (IoT). As two promising technologies, cognitive radio (CR) and radio …
An optimal transport-based federated reinforcement learning approach for resource allocation in cloud-edge collaborative iot
D Gan, X Ge, Q Li - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
In the traditional cloud–edge collaborative Internet of Things (IoT), the high-communication
cost and slow convergence of the models often result in high-delay and energy …
cost and slow convergence of the models often result in high-delay and energy …
Federated double deep Q-learning for joint delay and energy minimization in IoT networks
S Zarandi, H Tabassum - 2021 IEEE International Conference …, 2021 - ieeexplore.ieee.org
In this paper, we propose a federated deep reinforcement learning framework to solve a
multi-objective optimization problem, where we consider minimizing the expected long-term …
multi-objective optimization problem, where we consider minimizing the expected long-term …
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 …
An actor-critic deep reinforcement learning approach for transmission scheduling in cognitive internet of things systems
H Yang, X Xie - IEEE Systems Journal, 2019 - ieeexplore.ieee.org
The cognitive Internet of Things (CIoT) has attracted much interest recently in wireless
networks due to its wide applications in smart cities, intelligent transportation systems, and …
networks due to its wide applications in smart cities, intelligent transportation systems, and …
Deep multiagent reinforcement-learning-based resource allocation for internet of controllable things
B Gu, X Zhang, Z Lin, M Alazab - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Ultrareliable and low-latency communication (URLLC) is a prerequisite for the successful
implementation of the Internet of Controllable Things. In this article, we investigate the …
implementation of the Internet of Controllable Things. In this article, we investigate the …