Learning to coordinate in mobile-edge computing for decentralized task offloading
Edge servers, which are located in close proximity to mobile users, have become emerging
components for computation offloading in multiple Internet of Things (IoT) applications. As …
components for computation offloading in multiple Internet of Things (IoT) applications. As …
Deep reinforcement learning for autonomous internet of things: Model, applications and challenges
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …
around the world, where the IoT devices collect and share information to reflect status of the …
Hfdrl: An intelligent dynamic cooperate cashing method based on hierarchical federated deep reinforcement learning in edge-enabled iot
F Majidi, MR Khayyambashi… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) has significantly increased the number of terminals and network
traffic. It is necessary to exploit the full capacity of the network and optimize content transfer …
traffic. It is necessary to exploit the full capacity of the network and optimize content transfer …
Multiuser resource control with deep reinforcement learning in IoT edge computing
By leveraging the concept of mobile edge computing (MEC), massive amount of data
generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC …
generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC …
Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network
The Internet of Things (IoT) edge network has connected lots of heterogeneous smart
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
Energy-aware task allocation for mobile IoT by online reinforcement learning
Fog-aided Internet of Things (IoT) networks provide low latency IoT services by offloading
computational intensive and delay sensitive tasks to the fog nodes, which are deployed …
computational intensive and delay sensitive tasks to the fog nodes, which are deployed …
Online microservice orchestration for IoT via multiobjective deep reinforcement learning
By providing loosely coupled, lightweight, and independent services, the microservice
architecture is promising for large-scale and complex service provision requirements in the …
architecture is promising for large-scale and complex service provision requirements in the …
Smart resource allocation for mobile edge computing: A deep reinforcement learning approach
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …
capabilities has brought about a proliferation of numerous complex and computation …
Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
Edge-based federated deep reinforcement learning for IoT traffic management
A Jarwan, M Ibnkahla - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
The wide adoption of large-scale Internet of Things (IoT) systems has led to an
unprecedented increase in backhaul (BH) traffic congestion, making it critical to optimize …
unprecedented increase in backhaul (BH) traffic congestion, making it critical to optimize …