Smart resource allocation for mobile edge computing: A deep reinforcement learning approach

J Wang, L Zhao, J Liu, N Kato - IEEE Transactions on emerging …, 2019 - ieeexplore.ieee.org
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …

Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA

T Alfakih, MM Hassan, A Gumaei, C Savaglio… - Ieee …, 2020 - ieeexplore.ieee.org
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 …

A novel framework for mobile-edge computing by optimizing task offloading

A Naouri, H Wu, NA Nouri, S Dhelim… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
With the emergence of mobile computing offloading paradigms, such as mobile-edge
computing (MEC), many Internet of Things applications can take advantage of the computing …

Two-stage offloading optimization for energy–latency tradeoff with mobile edge computing in maritime Internet of Things

T Yang, H Feng, S Gao, Z Jiang, M Qin… - IEEE Internet of …, 2019 - ieeexplore.ieee.org
The ever-increasing growth in maritime activities with large amounts of Maritime Internet-of-
Things (M-IoT) devices and the exploration of ocean network leads to a great challenge for …

Multi-server multi-user multi-task computation offloading for mobile edge computing networks

L Huang, X Feng, L Zhang, L Qian, Y Wu - Sensors, 2019 - mdpi.com
This paper studies mobile edge computing (MEC) networks where multiple wireless devices
(WDs) offload their computation tasks to multiple edge servers and one cloud server …

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) …

Energy efficiency based joint computation offloading and resource allocation in multi-access MEC systems

X Yang, X Yu, H Huang, H Zhu - IEEE Access, 2019 - ieeexplore.ieee.org
With the rapid growth of computation demands from mobile applications, mobile-edge
computing (MEC) provides a new method to meet requirement of high data rate and high …

BC-EdgeFL: A defensive transmission model based on blockchain-assisted reinforced federated learning in IIoT environment

P Zhang, Y Hong, N Kumar, M Alazab… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Under the times of the Industrial Internet of Things, the traditional centralized machine
learning management method cannot deal with such huge data streams, and the problem of …

Contract-based computing resource management via deep reinforcement learning in vehicular fog computing

J Zhao, M Kong, Q Li, X Sun - IEEE Access, 2019 - ieeexplore.ieee.org
Vehicle fog computing (VFC) is proposed as a solution that can significantly reduce the task
processing overload of base station during the peak time, where the vehicle as a fog node …

Routing in fog-enabled IoT platforms: A survey and an SDN-based solution

FY Okay, S Ozdemir - IEEE Internet of Things Journal, 2018 - ieeexplore.ieee.org
Fog computing is a promising technology that helps overcome to the difficulties of handling a
huge amount of Internet of Things (IoT) data by distributing its applications and services …