AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future Directions
The cloud-edge-terminal collaborative network (CETCN) is considered as a novel paradigm
for emerging applications owing to its huge potential in providing low-latency and ultra …
for emerging applications owing to its huge potential in providing low-latency and ultra …
A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions
Abstract The fifth-generation (5G) network is considered a game-changing technology that
promises advanced connectivity for businesses and growth opportunities. To gain a …
promises advanced connectivity for businesses and growth opportunities. To gain a …
Co-evolution with deep reinforcement learning for energy-aware distributed heterogeneous flexible job shop scheduling
Energy-aware distributed heterogeneous flexible job shop scheduling (DHFJS) problem is
an extension of the traditional FJS, which is harder to solve. This work aims to minimize total …
an extension of the traditional FJS, which is harder to solve. This work aims to minimize total …
Double dqn-based coevolution for green distributed heterogeneous hybrid flowshop scheduling with multiple priorities of jobs
Distributed manufacturing involving heterogeneous factories presents significant challenges
to enterprises. Furthermore, the need to prioritize various jobs based on order urgency and …
to enterprises. Furthermore, the need to prioritize various jobs based on order urgency and …
Resource provisioning for mitigating edge DDoS attacks in MEC-enabled SDVN
Vehicular ad hoc network (VANET) has become an accessible technology for improving
road safety and driving experience, the problems of heterogeneity and lack of resources it …
road safety and driving experience, the problems of heterogeneity and lack of resources it …
DDNSAS: Deep reinforcement learning based deep Q-learning network for smart agriculture system
GG Devarajan, SM Nagarajan, TV Ramana… - … Informatics and Systems, 2023 - Elsevier
As the global population continues to grow and environmental conditions become
increasingly unpredictable, meeting the demands for food becomes increasingly difficult. To …
increasingly unpredictable, meeting the demands for food becomes increasingly difficult. To …
Machine Learning Empowered Emerging Wireless Networks in 6G: Recent Advancements, Challenges & Future Trends
The upcoming 6G networks are sixth-sense next-generation communication networks with
an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a …
an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a …
Deep reinforcement learning based cooperative partial task offloading and resource allocation for IIoT applications
The Industrial Internet of Things (IIoT) has been regarded as one of the pillars supporting the
conceptual paradigm of the Industry 4.0. Compared with traditional cloud computing …
conceptual paradigm of the Industry 4.0. Compared with traditional cloud computing …
Computation Rate Maximization for SCMA-Aided Edge Computing in IoT Networks: A Multi-Agent Reinforcement Learning Approach
Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the
Internet of Things (IoT) networks can enable efficient connectivity and timely computation for …
Internet of Things (IoT) networks can enable efficient connectivity and timely computation for …
A DRL-driven intelligent optimization strategy for resource allocation in cloud-edge-end cooperation environments
Complex dynamic services and heterogeneous network environments make the
asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet …
asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet …