Autoencoder and Teaching-learning-based Optimizer for Mobile Edge Computing System Optimization Problems
By using an autoencoder as a dimension reduction tool, an Autoencoder-embedded
Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving …
Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving …
Joint optimization of energy consumption and latency in mobile edge computing for Internet of Things
With wide adoption of Internet of Things (IoT) across the world, the IoT devices are facing
more and more intensive computation task nowadays. However, the IoT devices are usually …
more and more intensive computation task nowadays. However, the IoT devices are usually …
Computation offloading policy for machine learning in mobile edge computing environments
M GUO, J ZHANG - Journal of Computer Applications, 2021 - joca.cn
Concerning the challenges of the diversity of data sources, non-independent and identical
distribution of data and the heterogeneity of both computing capabilities and energy …
distribution of data and the heterogeneity of both computing capabilities and energy …
A Survey on mobile edge computing for deep learning
P Choi, J Kwak - 2023 International Conference on Information …, 2023 - ieeexplore.ieee.org
Deep learning-based services such as AI assistants and self-driving cars are of great
interest in academia and industry because of their unrivaled performance. Because these …
interest in academia and industry because of their unrivaled performance. Because these …
Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems
Evolutionary algorithms and swarm intelligence ones are commonly used to solve many
complex optimization problems in different fields. Yet, some of them have limited …
complex optimization problems in different fields. Yet, some of them have limited …
A Comprehensive Review of Optimisation Techniques in Machine Learning for Edge Devices
P Alwin Infant, PN Renjith, GR Jainish… - Mobile Computing and …, 2022 - Springer
Hundreds of billions of connected IoT devices will populate the earth in future. The
environment interacts with devices with restricted resources. Machine learning models will …
environment interacts with devices with restricted resources. Machine learning models will …
Partial offloading strategy for mobile edge computing considering mixed overhead of time and energy
Mobile edge computing (MEC) utilizes wireless access network to provide powerful
computing resources for mobile users to improve the user experience, which mainly …
computing resources for mobile users to improve the user experience, which mainly …
Federated learning-based computation offloading optimization in edge computing-supported internet of things
Recent visualizations of smart cities, factories, healthcare system and etc. raise challenges
on the capability and connectivity of massive Internet of Things (IoT) devices. Hence, edge …
on the capability and connectivity of massive Internet of Things (IoT) devices. Hence, edge …
An efficient online computation offloading approach for large-scale mobile edge computing via deep reinforcement learning
Mobile edge computing (MEC) has been envisioned as a promising paradigm that could
effectively enhance the computational capacity of wireless user devices (WUDs) and quality …
effectively enhance the computational capacity of wireless user devices (WUDs) and quality …
Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing.
Y Wei, Z Wang, D Guo, FR Yu - Computers, Materials & …, 2019 - search.ebscohost.com
To reduce the transmission latency and mitigate the backhaul burden of the centralized
cloud-based network services, the mobile edge computing (MEC) has been drawing …
cloud-based network services, the mobile edge computing (MEC) has been drawing …