Collaborate edge and cloud computing with distributed deep learning for smart city internet of things
City Internet-of-Things (IoT) applications are becoming increasingly complicated and thus
require large amounts of computational resources and strict latency requirements. Mobile …
require large amounts of computational resources and strict latency requirements. Mobile …
DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing
With the explosive growth of mobile data and the unprecedented demand for computing
power, resource-constrained edge devices cannot effectively meet the requirements of …
power, resource-constrained edge devices cannot effectively meet the requirements of …
MR-DRO: A fast and efficient task offloading algorithm in heterogeneous edge/cloud computing environments
With the rapid development of Internet of Things (IoT) and next-generation communication
technologies, resource-constrained mobile devices (MDs) fail to meet the demand of …
technologies, resource-constrained mobile devices (MDs) fail to meet the demand of …
Deep learning empowered task offloading for mobile edge computing in urban informatics
Led by industrialization of smart cities, numerous interconnected mobile devices, and novel
applications have emerged in the urban environment, providing great opportunities to …
applications have emerged in the urban environment, providing great opportunities to …
Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA
In recent years, computation offloading has become an effective way to overcome the
constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive …
constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive …
End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment
With the drastic development of Internet of things, the number of connected mobile users
(MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has …
(MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has …
A computation offloading method over big data for IoT-enabled cloud-edge computing
The Internet of mobile things is a burgeoning technique that generates, stores and
processes big real-time data to render rich services for mobile users. In order to mitigate …
processes big real-time data to render rich services for mobile users. In order to mitigate …
Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability
Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-
latency, high-bandwidth and agile mobile services by deploying computing platform at the …
latency, high-bandwidth and agile mobile services by deploying computing platform at the …
Distributed edge computing offloading algorithm based on deep reinforcement learning
Y Li, F Qi, Z Wang, X Yu, S Shao - IEEE Access, 2020 - ieeexplore.ieee.org
As a mode of processing task request, edge computing paradigm can reduce task delay and
effectively alleviate network congestion caused by the proliferation of Internet of things (IoT) …
effectively alleviate network congestion caused by the proliferation of Internet of things (IoT) …
Deep reinforcement learning‐based multitask hybrid computing offloading for multiaccess edge computing
J Cai, H Fu, Y Liu - International Journal of Intelligent Systems, 2022 - Wiley Online Library
By deploying computing units in edge servers, the device‐generated computation‐intensive
tasks can be offloaded from the cloud, lessening the core network's traffic and reducing the …
tasks can be offloaded from the cloud, lessening the core network's traffic and reducing the …