Task offloading based on LSTM prediction and deep reinforcement learning for efficient edge computing in IoT

Y Tu, H Chen, L Yan, X Zhou - Future Internet, 2022 - mdpi.com
In IoT (Internet of Things) edge computing, task offloading can lead to additional
transmission delays and transmission energy consumption. To reduce the cost of resources …

Cloud-edge collaboration in industrial internet of things: A joint offloading scheme based on resource prediction

Z Sun, H Yang, C Li, Q Yao, D Wang… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
With the continuous addition of an abundant of heterogeneous devices, the limitation of task
delay has become an obstacle to the development of the Industrial Internet of Things (IIoT) …

Efficient data offloading using markovian decision on state reward action in edge computing

M Li, H Lei, H Guo, R Sulaiman, W Deebani… - Journal of Grid …, 2023 - Springer
Efficient planning of Task offloading in edge computing for the Internet of Things (IoT) can
increase latency issues and power use. In this research, we propose the task offloading …

A Meta Reinforcement Learning-Based Task Offloading Strategy for IoT Devices in an Edge Cloud Computing Environment

H Yang, W Ding, Q Min, Z Dai, Q Jiang, C Gu - Applied Sciences, 2023 - mdpi.com
Developing an effective task offloading strategy has been a focus of research to improve the
task processing speed of IoT devices in recent years. Some of the reinforcement learning …

[HTML][HTML] Edge computational task offloading scheme using reinforcement learning for IIoT scenario

MS Hossain, CI Nwakanma, JM Lee, DS Kim - ICT Express, 2020 - Elsevier
In this paper, end devices are considered here as agent, which makes its decisions on
whether the network will offload the computation tasks to the edge devices or not. To tackle …

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) …

DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing

G Qu, H Wu, R Li, P Jiao - IEEE Transactions on Network and …, 2021 - ieeexplore.ieee.org
With the explosive growth of mobile data and the unprecedented demand for computing
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

Z Zhang, N Wang, H Wu, C Tang… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
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 …

Intelligent delay-aware partial computing task offloading for multiuser industrial internet of things through edge computing

X Deng, J Yin, P Guan, NN Xiong… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely
changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a …

Multitask multiobjective deep reinforcement learning-based computation offloading method for industrial Internet of Things

J Cai, H Fu, Y Liu - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Edge computing has emerged as a promising paradigm to deploy computing resources to
the network edge. However, most existing computation offloading strategies consider only …