Urbanenqosplace: A deep reinforcement learning model for service placement of real-time smart city iot applications
… Abstract—Multi-access Edge Computing (MEC) enables IoT applications to place their
services in the edge servers of mobile networks, balancing Quality-of-Service (QoS) and energy-…
services in the edge servers of mobile networks, balancing Quality-of-Service (QoS) and energy-…
A deep reinforcement learning-based caching strategy for iot networks with transient data
… To better capture the regional-different popularity distribution, we adopt a hierarchical
architecture to deploy edge caching nodes in IoT networks. The results of comprehensive …
architecture to deploy edge caching nodes in IoT networks. The results of comprehensive …
A trust and energy-aware double deep reinforcement learning scheduling strategy for federated learning on IoT devices
… the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that
… Thereafter, we design a Double Deep Q Learning (DDQN)-based scheduling algorithm that …
… Thereafter, we design a Double Deep Q Learning (DDQN)-based scheduling algorithm that …
Deep reinforcement learning for energy-efficient task scheduling in SDN-based IoT network
… However, edge nodes are often faced with issues to perform optimal resource distribution …
Deep Reinforcement Learning (DRL) approach for IoT traffic scheduling at the network edge. …
Deep Reinforcement Learning (DRL) approach for IoT traffic scheduling at the network edge. …
DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing
… that when compared with traditional Deep Reinforcement Learning (DRL) algorithms, the …
we design an edge-cloud offloading framework in this paper, where IoT devices can choose …
we design an edge-cloud offloading framework in this paper, where IoT devices can choose …
Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor–critic deep reinforcement learning
… to the edge has been proposed to accommodate future IoT services, and the fog-enabled IoT
is … Inheriting the cloud architecture, the fog-enabled IoT enable edge equipments [eg, base …
is … Inheriting the cloud architecture, the fog-enabled IoT enable edge equipments [eg, base …
Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach
… The deep reinforcement learning improves significantly learning speed when the edge …
For instance, there are thousands of devices in the Internet of things system and the network …
For instance, there are thousands of devices in the Internet of things system and the network …
[HTML][HTML] Deep reinforcement learning multi-agent system for resource allocation in industrial internet of things
J Rosenberger, M Urlaub, F Rauterberg, T Lutz, A Selig… - Sensors, 2022 - mdpi.com
… Objective: The main objective in this work is to maximize the edge computing using
available edge resource rather than minimizing routing and computation delays [11,14]. …
available edge resource rather than minimizing routing and computation delays [11,14]. …
When edge computing meets microgrid: A deep reinforcement learning approach
… Sequentially, we use the output of the first subproblem as a input for solving the second
subproblem, where we apply a model-based deep reinforcement learning (MDRL). Finally, the …
subproblem, where we apply a model-based deep reinforcement learning (MDRL). Finally, the …
IoT microservice deployment in edge-cloud hybrid environment using reinforcement learning
… Furthermore, we propose MB DDPG, a Deep Reinforcement Learning method, to solve
the optimization problem in the hybrid environment. We contrast MB DDPG with random …
the optimization problem in the hybrid environment. We contrast MB DDPG with random …