Literature Database Entry
li2022data
Kai Li, Wei Ni, Yousef Emami and Falko Dressler, "Data-driven Flight Control of Internet-of-Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning," IEEE Wireless Communications, Artificial Intelligence Enabled Internet of UAVs Communications, vol. 29 (4), pp. 18–23, August 2022.
Abstract
Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.
Quick access
Original Version (at publishers web site)
Authors' Version (PDF on this web site)
BibTeX
Contact
Kai Li
Wei Ni
Yousef Emami
Falko Dressler
BibTeX reference
@article{li2022data,
author = {Li, Kai and Ni, Wei and Emami, Yousef and Dressler, Falko},
doi = {10.1109/MWC.002.2100681},
title = {{Data-driven Flight Control of Internet-of-Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning}},
pages = {18--23},
journal = {IEEE Wireless Communications, Artificial Intelligence Enabled Internet of UAVs Communications},
issn = {1536-1284},
publisher = {IEEE},
month = {8},
number = {4},
volume = {29},
year = {2022},
}
Copyright notice
Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.
The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.
The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.
This page was automatically generated using BibDB and bib2web.