Fairness-Aware Task Loss Rate Minimization for Multi-UAV Enabled Mobile Edge Computing
Author:
Affiliation:
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
2. Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
Funder
National Natural Science Foundation of China
Quzhou Government
Sichuan Major Research and Development Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/5962382/10008400/09932868.pdf?arnumber=9932868
Reference10 articles.
1. Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning
2. Energy-Efficient UAV Deployment and Task Scheduling in Multi-UAV Edge Computing
3. Multi-agent actor-critic for mixed cooperative-competitive environments;lowe;Proc NIPS,2017
4. Online Trajectory and Resource Optimization for Stochastic UAV-Enabled MEC Systems
5. Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing
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4. Fairness-Aware Computation Offloading With Trajectory Optimization and Phase-Shift Design in RIS-Assisted Multi-UAV MEC Network;IEEE Internet of Things Journal;2024-06-01
5. Fairness-Aware Computation Efficiency Maximization for Multi-UAV-Enabled MEC System;Proceedings of the 2023 International Conference on Communication Network and Machine Learning;2023-10-27
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