High-Performance UAV Crowdsensing: A Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

High-Performance UAV Crowdsensing: A Deep Reinforcement Learning Approach

Published:2022-10-01 Issue:19 Volume:9 Page:18487-18499
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wei Kaimin¹^ORCID,Huang Kai¹^ORCID,Wu Yongdong¹^ORCID,Li Zhetao¹^ORCID,He Hongliang¹^ORCID,Zhang Jilian¹,Chen Jinpeng²^ORCID,Guo Song³^ORCID

Affiliation:

1. College of Information Science and Technology, National and Local Joint Engineering Research Center of Network Security Detection and Protection Technology, Guangdong Provincial Key Laboratory of Data Security and Privacy Protection, Jinan University, Guangzhou, China

2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

3. Department of Computer, Hong Kong Polytechnic University, Hong Kong

Funder

National Natural Science Foundation of China

Guangdong Key Research and Development Plan 2020

National Key Research and Development Plan 2020

Basic and Applied Basic Research Foundation of Guangdong Province

Hunan Science and Technology Planning Project

Hong Kong RGC Research Impact Fund

General Research Fund

Shenzhen Science and Technology Innovation Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9899827/09738818.pdf?arnumber=9738818

Reference22 articles.

1. Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach

2. Multi-agent actor-critic for mixed cooperative-competitive environments;lowe;arXiv 1706 02275,2017

3. Planning and acting in partially observable stochastic domains

4. Minimizing age of information in vehicular networks

5. Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

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