Intelligent and Interactive Healthcare System (I2HS) Using Machine Learning

R Raina, RK Jha - IEEE Access, 2022 - ieeexplore.ieee.org
There has been a gigantic stir in the world's healthcare sector for the past couple of years
with the advent of the Covid-19 pandemic. The healthcare system has suffered a major …

AI 算法在车联网通信与计算中的应用综述

康宇, 刘雅琼, 赵彤雨, 寿国础 - 电信科学, 2023 - infocomm-journal.com
在5G 时代, 车联网的通信和计算发展受到信息量急速增加的限制. 将AI 算法应用在车联网,
可以实现车联网通信和计算方面的新突破. 调研了AI 算法在通信安全, 通信资源分配 …

A Survey on Integrating Edge Computing With AI and Blockchain in Maritime Domain, Aerial Systems, IoT, and Industry 4.0

A Alanhdi, L Toka - IEEE Access, 2024 - ieeexplore.ieee.org
In terms of digital transformation, organizations today are aware of the critical role that data
and information play in their expansion and development in light of the Internet of Things. To …

Federated Deep Reinforcement Learning for Task Offloading in Digital Twin Edge Networks

Y Dai, J Zhao, J Zhang, Y Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Digital twin edge networks provide a new paradigm that combines mobile edge computing
(MEC) and digital twins to improve network performance and reduce communication cost by …

Dynamic Task Allocation for Robotic Edge System Resilience Using Deep Reinforcement Learning

M Afrin, J Jin, A Rahman, S Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Incorporating edge and cloud computing with robotics provides extended options for robots
to perform real-time sensing and actuation operations in various cyber–physical systems …

CASIT: Collective Intelligent Agent System for Internet of Things

N Zhong, Y Wang, R Xiong, Y Zheng… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
In the last few years, the bottleneck of bandwidth in Internet of Thing (IoT) has driven
expectations to figure out new ways to preprocess the information needed to be transmitted …

Computation offloading and resource management for energy and cost trade-offs with deep reinforcement learning in mobile edge computing

R Mo, X Xu, X Zhang, L Qi, Q Liu - … 2021, Virtual Event, November 22–25 …, 2021 - Springer
Mobile edge computing, as a formidable paradigm, sinks the computing and communication
resources from the centralized cloud to the edge of networks near to users, which meets the …

Inverse Reinforcement Learning with Graph Neural Networks for Full-Dimensional Task Offloading in Edge Computing

G Wang, P Cheng, Z Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The ever-increasing number of ubiquitous Internet of Things (IoT) applications entails a high
demand for scarce communication and network resources. To meet this stringent …

iCOS: A deep reinforcement learning scheme for wireless-charged MEC networks

C Wan, S Guo, J He, G Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Computation offloading is an effective method in mobile edge computing (MEC) to relieve
user equipment (UE) from the limited computation resource and battery capacity …

A Stackelberg Game Based Framework for Edge Pricing and Resource Allocation in Mobile Edge Computing

S Cheng, T Ren, H Zhang, J Huang… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Nowadays, Mobile Edge Computing (MEC) appears as a new computing paradigm with its
ability to utilize the computing power of both local devices and edge servers. In MEC, edge …