Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
Dynamic task offloading for mobile edge computing with hybrid energy supply
Y Chen, F Zhao, Y Lu, X Chen - Tsinghua Science and …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC), as a new distributed computing model, satisfies the low
energy consumption and low latency requirements of computation-intensive services. The …
energy consumption and low latency requirements of computation-intensive services. The …
Classification of resource management approaches in fog/edge paradigm and future research prospects: A systematic review
The fog paradigm extends the cloud capabilities at the edge of the network. Fog computing-
based real-time applications (Online gaming, 5G, Healthcare 4.0, Industrial IoT, autonomous …
based real-time applications (Online gaming, 5G, Healthcare 4.0, Industrial IoT, autonomous …
Dynamic admission control and resource allocation for mobile edge computing enabled small cell network
Mobile edge computing (MEC) has recently risen as a promising paradigm to meet the
increasing resource requirements of the terminal devices. Meanwhile, small cell network …
increasing resource requirements of the terminal devices. Meanwhile, small cell network …
Dynamic offloading and resource scheduling for mobile-edge computing with energy harvesting devices
Driven by Internet of Things (IoT) and 5G communication technologies, the paradigm of
mobile computing has changed from centralized mobile cloud computing to distributed …
mobile computing has changed from centralized mobile cloud computing to distributed …
Trust-driven reinforcement selection strategy for federated learning on IoT devices
Federated learning is a distributed machine learning approach that enables a large number
of edge/end devices to perform on-device training for a single machine learning model …
of edge/end devices to perform on-device training for a single machine learning model …
Improved butterfly optimization algorithm for data placement and scheduling in edge computing environments
Mobile edge computing (MEC) is an interesting technology aimed at providing various
processing and storage resources at the edge of mobile devices (MDs). However, MECs …
processing and storage resources at the edge of mobile devices (MDs). However, MECs …
Application of Quantum Particle Swarm Optimization for task scheduling in Device-Edge-Cloud Cooperative Computing
B Wang, Z Zhang, Y Song, M Chen, Y Chu - Engineering Applications of …, 2023 - Elsevier
Swarm intelligence and evolutionary algorithms (SI&EAs) have been widely applied to
various fields. In this paper, we make the first attempt, to our best knowledge, to apply an …
various fields. In this paper, we make the first attempt, to our best knowledge, to apply an …
Artificial intelligence and blockchain-assisted offloading approach for data availability maximization in edge nodes
G Manogaran, S Mumtaz… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Mobile Edge Computing (MEC) paradigm is designed to meet the user requirements by
providing cloud services at the edge of the user network. Blockchain technology with the …
providing cloud services at the edge of the user network. Blockchain technology with the …
Deep Q-network based resource allocation for UAV-assisted ultra-dense networks
With the rapid development of the fifth-generation (5G) wireless communications, the
number of users is increasing dramatically and Ultra-Dense Networks (UDN) are becoming …
number of users is increasing dramatically and Ultra-Dense Networks (UDN) are becoming …