Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

S. Vimal; Manju Khari; Nilanjan Dey; Rubén González Crespo; Harold Robinson

doi:10.1016/j.comcom.2020.01.018

ScienceGate Book Chapters

JOURNAL ARTICLE

Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

S. Vimal Manju Khari Nilanjan Dey Rubén González Crespo Harold Robinson

Year: 2020 Journal: Computer Communications Vol: 151 Pages: 355-364 Publisher: Elsevier BV

DOI: 10.1016/j.comcom.2020.01.018

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Reinforcement learning Mobile edge computing Distributed computing Resource allocation Edge computing Computer network Enhanced Data Rates for GSM Evolution Artificial intelligence

Metrics

127

Cited By

17.46

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Caching and Content Delivery

Physical Sciences → Computer Science → Computer Networks and Communications

Cloud Computing and Resource Management

Physical Sciences → Computer Science → Information Systems

Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

Metrics

Citation History

Topics

Related Documents

Deep Reinforcement Learning Based Computing Offloading and Resource Allocation Algorithm for Mobile Edge Networks

Optimal Resource Allocation in Mobile Edge Computing using Reinforcement Learning Approach

Reinforcement Learning-Based Resource Allocation in Edge Computing

Deep Reinforcement Learning-Based Algorithm for Dynamic Resource Allocation in Edge Computing

Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization