Deep reinforcement learning for Internet of Things: A comprehensive survey

W Chen, X Qiu, T Cai, HN Dai… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

A survey on computation offloading modeling for edge computing

H Lin, S Zeadally, Z Chen, H Labiod, L Wang - Journal of Network and …, 2020 - Elsevier
As a promising technology, edge computing extends computation, communication, and
storage facilities toward the edge of a network. This new computing paradigm opens up new …

Deep Reinforcement Learning for energy-aware task offloading in join SDN-Blockchain 5G massive IoT edge network

B Sellami, A Hakiri, SB Yahia - Future Generation Computer Systems, 2022 - Elsevier
Abstract The Internet-of-Things (IoT) edge allows cloud computing services for topology and
location-sensitive distributed computing. As an immediate benefit, it improves network …

Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge–cloud computing environments

A Jayanetti, S Halgamuge, R Buyya - Future Generation Computer Systems, 2022 - Elsevier
The wide-spread embracement and integration of Internet of Things (IoT) has inevitably lead
to an explosion in the number of IoT devices. This in turn has led to the generation of …

Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network

B Sellami, A Hakiri, SB Yahia, P Berthou - Computer Networks, 2022 - Elsevier
Abstract The fifth-generation (5G) mobile network services have made tremendous growth in
the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are …

Smart healthcare: RL-based task offloading scheme for edge-enable sensor networks

R Yadav, W Zhang, IA Elgendy, G Dong… - IEEE Sensors …, 2021 - ieeexplore.ieee.org
With the wide application of Internet-of-Medical-Things (IoMTs) or sensor nodes which
equipped with sensors. These networked sensors are used to gather enormous data from …

[HTML][HTML] Recent advances in collaborative scheduling of computing tasks in an edge computing paradigm

S Chen, Q Li, M Zhou, A Abusorrah - Sensors, 2021 - mdpi.com
In edge computing, edge devices can offload their overloaded computing tasks to an edge
server. This can give full play to an edge server's advantages in computing and storage, and …

Energy-efficient fog computing for 6G-enabled massive IoT: Recent trends and future opportunities

UM Malik, MA Javed, S Zeadally… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Fog computing is a promising technology that can provide storage and computational
services to future 6G networks. To support the massive Internet-of-Things (IoT) applications …

[HTML][HTML] Integration of network slicing and machine learning into edge networks for low-latency services in 5G and beyond systems

A Domeke, B Cimoli, IT Monroy - Applied Sciences, 2022 - mdpi.com
Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging
applications having diverse and strict quality of service (QoS) requirements. To meet ultra …

Reinforcement learning framework for server placement and workload allocation in multiaccess edge computing

A Mazloomi, H Sami, J Bentahar… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Cloud computing is a reliable solution to provide distributed computation power. However,
real-time response is still challenging regarding the enormous amount of data generated by …