Optimizing Drone Energy Use for Emergency Communications in Disasters via Deep Reinforcement Learning

Wen Qiu, Xun Shao, Hiroshi Masui () and William Liu
Additional contact information
Wen Qiu: Information Processing Center, Kitami Institute of Technology, Kitami 090-8507, Japan
Xun Shao: Department of Electrical and Electronic Information Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan
Hiroshi Masui: Information Processing Center, Kitami Institute of Technology, Kitami 090-8507, Japan
William Liu: Department of Information Technology and Software Engineering, School of Engineering, Computer and Mathematical Sciences, Unitec Institute of Technology, Auckland 1025, New Zealand

Future Internet, 2024, vol. 16, issue 7, 1-18

Abstract: For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration of UAVs remains a major challenge due to limitations in their communication range and energy capacity. In addition, the relatively high cost of drones and the issue of mutual communication interference make it impractical to deploy an unlimited number of drones in a given area. To maximize the communication services provided by a limited number of drones to the ground user equipment (UE) within a certain time frame while minimizing the drone energy consumption, we propose a multi-agent proximal policy optimization (MAPPO) algorithm. Considering the dynamic nature of the environment, we analyze diverse observation data structures and design novel objective functions to enhance the drone performance. We find that, when drone energy consumption is used as a penalty term in the objective function, the drones—acting as agents—can identify the optimal trajectory that maximizes the UE coverage while minimizing the energy consumption. At the same time, the experimental results reveal that, without considering the machine computing power required for training and convergence time, the proposed key algorithm demonstrates better performance in communication coverage and energy saving as compared with other methods. The average coverage performance is 10 – 45 % higher than that of the other three methods, and it can save up to 3 % more energy.

Keywords: drones; multi-agent deep reinforcement learning (MADRL); energy optimization; emergency communications (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/7/245/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/7/245/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:7:p:245-:d:1432815

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().