Dima: Distributed cooperative microservice caching for internet of things in edge computing by deep reinforcement learning
… Thus, in this paper, we propose a deep reinforcement learning (DRL) based distributed
cooperative microservice caching scheme, named DIMA. Recently, DRL, as a nature-inspired …
cooperative microservice caching scheme, named DIMA. Recently, DRL, as a nature-inspired …
NOMA assisted multi-task multi-access mobile edge computing via deep reinforcement learning for industrial Internet of Things
… the total energy consumption of IoT device to complete its tasks … gains from the IoT device
to the edge-computing servers are … , which is based on deep reinforcement learning (DRL), to …
to the edge-computing servers are … , which is based on deep reinforcement learning (DRL), to …
Collaborative computation offloading and resource allocation in multi-UAV-assisted IoT networks: A deep reinforcement learning approach
AM Seid, GO Boateng, S Anokye… - … Internet of Things …, 2021 - ieeexplore.ieee.org
… We propose model-free deep reinforcement learning (DRL)-based … -edge computing (MEC)
paradigm, which brings the computing resources closer to IoT devices at the edge network. …
paradigm, which brings the computing resources closer to IoT devices at the edge network. …
Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks
… Abstract—Mobile Edge Computing (MEC) is a promising technology … edge of Internet of
Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT …
Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT …
Exploring deep-reinforcement-learning-assisted federated learning for online resource allocation in privacy-preserving edgeiot
… devices and their transmit powers in an FL-empowered edge IoT system, thereby balancing
… CONCLUSION In this paper, we proposed FL-DLT3, which is a new deep reinforcement …
… CONCLUSION In this paper, we proposed FL-DLT3, which is a new deep reinforcement …
Edge caching for IoT transient data using deep reinforcement learning
… edge caching based Deep Reinforcement Learning (DRL) is studied to balance the
communication cost and freshness loss in [15]. In this paper, we investigate IoT … data and multiple …
communication cost and freshness loss in [15]. In this paper, we investigate IoT … data and multiple …
Caching transient data for Internet of Things: A deep reinforcement learning approach
… of deep reinforcement learning (DRL) to solve the problem of caching IoT data at the edge
without knowing future IoT … 1) We propose an edge caching-based IoT system framework for …
without knowing future IoT … 1) We propose an edge caching-based IoT system framework for …
Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network
… We do not use VMs in the edge IoT network since they increase the load in the IoT
infrastructure, in contrast to the logic to the Edge/Fog approach. Instead, we rely on lightweight IoT …
infrastructure, in contrast to the logic to the Edge/Fog approach. Instead, we rely on lightweight IoT …
Accuracy-guaranteed collaborative DNN inference in industrial IoT via deep reinforcement learning
… Things (IoT) devices and edge networks is essential to support computation-intensive deep
… Sampling rate adaption, which dynamically configures the sampling rates of industrial IoT …
… Sampling rate adaption, which dynamically configures the sampling rates of industrial IoT …
Federated deep reinforcement learning for traffic monitoring in SDN-based IoT networks
… • To maximize the traffic granularity degree for all IoT traffic groups while protecting the
SDN-based IoT edge nodes from the flow-table overflow problem, we develop a flow rule match-…
SDN-based IoT edge nodes from the flow-table overflow problem, we develop a flow rule match-…