Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions

T Zhang, Y Li, CLP Chen - Information Sciences, 2021 - Elsevier
Abstract Proliferation of Industrial Internet has dramatically changed the way we live and
work. It brings convenience to our society and sometimes requires real-time processing of …

Content caching in mobile edge computing: a survey

Y Khan, S Mustafa, RW Ahmad, T Maqsood… - Cluster …, 2024 - Springer
As wireless communication technology continues to advance, the number of intelligent
devices, such as computers, mobile phones, and iPads, is increasing rapidly. To keep up …

Servant: a user service requirements, timeslot sacrifice, and triple benefit-aware resource and worker provisioning scheme for digital twin and MEC enhanced 6G …

M Chowdhury - International Journal of Sensor Networks, 2023 - inderscienceonline.com
To minimise the application execution latencies of 6G applications, multi-access edge
computing (MEC) technology plays an indispensable role. The digital twin (DT) is another …

Deep Reinforcement Learning‐Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

Z Wan, Y Li - Wireless Communications and Mobile Computing, 2020 - Wiley Online Library
In the next‐generation wireless communications system of Beyond 5G networks, video
streaming services have held a surprising proportion of the whole network traffic …

Adaptive real-time offloading decision-making for mobile edges: deep reinforcement learning framework and simulation results

S Park, D Kwon, J Kim, YK Lee, S Cho - Applied Sciences, 2020 - mdpi.com
This paper proposes a novel dynamic offloading decision method which is inspired by deep
reinforcement learning (DRL). In order to realize real-time communications in mobile edge …

Mobile Edge Computing: Security and Privacy Issues, Challenges and Countermeasures.

MZ Alam, S Ahmed, H Khan, MI Alam… - IUP Journal of …, 2022 - search.ebscohost.com
Abstract Mobile Edge Computing (MEC) is a novel technology that provides fast data
processing with efficiency on 5G networks. It can be propitious in Internet of Things (IoT) …

Trends in 3D Point Cloud Contents Sampling in Mobile AR/VR Platforms

H Baek, H Lee, JY Kim, S Jung… - 2022 IEEE VTS Asia …, 2022 - ieeexplore.ieee.org
In recent years, point clouds have attracted increasing attention in a variety of industries,
such as autonomous driving and augmented reality. The point clouds offer a denser and …

GPU-specific task offloading in the mobile edge computing network

N Kim, Y Lee, C Lee, TV Nguyen… - … on Information and …, 2020 - ieeexplore.ieee.org
Graphics processing unit (GPU)-specific tasks can be done by mobile edge computing in 5G
networks because user equipments (UEs) offload the tasks near to Edge Server such as …

Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles

S Park, J Park, D Mohaisen… - … Conference on Information …, 2020 - ieeexplore.ieee.org
In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm
designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks …

Dynamic Resource Allocation and Streaming in Mobile Edges: A Deep Reinforcement Learning Approach

D Khan, Z Pervaiz - … , MLICOM 2020, Shenzhen, China, September 26-27 …, 2021 - Springer
Real-Time wireless communication devices with restricted assets face more extreme limit
limitations than at any other time expansion of various sophisticated and calculation severe …