Caching in dynamic IoT networks by deep reinforcement learning
… Note that our caching strategies in dynamic IoT networks can also be applied to mobile IoT
networks where the IoT devices are mobile if we assume that the IoT devices remain static …
networks where the IoT devices are mobile if we assume that the IoT devices remain static …
Deep reinforcement learning for Internet of Things: A comprehensive survey
… ) heterogeneous IoT devices and IoT data: various IoT devices can … IoT data; 3) complex
IoT networks: IoT composites of a great diversity of IoT networks enabled by different types of IoT …
IoT networks: IoT composites of a great diversity of IoT networks enabled by different types of IoT …
Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey
… it possible to deploy IoT networks in unreachable areas … when dealing with large-scale IoT
systems, and requires a … decision-making in wireless IoT networks to effectively address their …
systems, and requires a … decision-making in wireless IoT networks to effectively address their …
A deep reinforcement learning-based caching strategy for iot networks with transient data
… IoT data into account. To better capture the regional-different popularity distribution, we adopt
a hierarchical architecture to deploy edge caching nodes in IoT networks. … the IoT networks …
a hierarchical architecture to deploy edge caching nodes in IoT networks. … the IoT networks …
Applications of deep reinforcement learning in communications and networking: A survey
… -scale networks, eg, IoT systems with thousands of devices, DRL allows network controller or
IoT gate… access, and transmit power for a massive number of IoT devices and mobile users. …
IoT gate… access, and transmit power for a massive number of IoT devices and mobile users. …
iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
… However, the deep Qlearning algorithm … deep Q-learning directly in our network is difficult
to achieve a high service performance. Numeric results support our conclusion that the deep Q-…
to achieve a high service performance. Numeric results support our conclusion that the deep Q-…
Deep reinforcement learning for resource protection and real-time detection in IoT environment
… of the electronic devices in IoT environments. In this article, a fast deep-reinforcement-learning
(DRL… The development of the deep learning and the deep neural network accelerated the …
(DRL… The development of the deep learning and the deep neural network accelerated the …
A deep reinforcement learning-based multi-optimality routing scheme for dynamic IoT networks
… for routing optimization for dynamic IoT network. Although conventional machine learning
can break through some bottlenecks of traditional methods in the network routing, it still has …
can break through some bottlenecks of traditional methods in the network routing, it still has …
Deep reinforcement learning for fresh data collection in UAV-assisted IoT networks
… be a promising solution in Internet of Things (IoT), especially for … problem in UAV-assisted
IoT networks. Particularly, the UAV … algorithm based on deep reinforcement learning (DRL) is …
IoT networks. Particularly, the UAV … algorithm based on deep reinforcement learning (DRL) is …
AoI-energy-aware UAV-assisted data collection for IoT networks: A deep reinforcement learning method
… deep deterministic (TD3) policy gradient-based UAV trajectory planning algorithm (TD3AUTP)
by introducing the deep neural network … scheme outperforms the deep Q-network and actor…
by introducing the deep neural network … scheme outperforms the deep Q-network and actor…
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