Multi-UAV Assisted Network Coverage Optimization for Rescue Operations using Reinforcement Learning

Omar Sami Oubbati, Hakim Badis, Abderrezak Rachedi, Abderrahmane Lakas, Pascal Lorenz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Mobile communication networks could make a significant difference in rescuing affected people in post-disaster scenarios. However, the existing communication infrastructures tend to be out of service in such scenarios. To solve this issue, Unmanned Aerial Vehicles (UAVs) could be launched as flying base stations to provide the required coverage to Rescue Members (RMs) and allow them to communicate and transmit crucial information through the established links. Meanwhile, with the unpredictable movements of RMs, three serious issues are affecting the deployment of UAVs: (i) the control of their mobility, (ii) their limited energy capacity, and (iii) their restricted communication ranges. Aiming to address these issues, we propose deploying an intelligent connected group of energy-efficient UAVs assisting RMs and providing them communication coverage in the long run. These requirements are satisfied using a deep reinforcement learning strategy to learn the environment dynamics and make good trajectory decisions. Simulation experiments have demonstrated the potential of our framework compared to baseline methods to provide temporary communication networks for emergency response teams during disaster relief missions.

Original languageEnglish
Title of host publication2023 IEEE 20th Consumer Communications and Networking Conference, CCNC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1003-1008
Number of pages6
ISBN (Electronic)9781665497343
DOIs
Publication statusPublished - 2023
Event20th IEEE Consumer Communications and Networking Conference, CCNC 2023 - Las Vegas, United States
Duration: Jan 8 2023Jan 11 2023

Publication series

NameProceedings - IEEE Consumer Communications and Networking Conference, CCNC
Volume2023-January
ISSN (Print)2331-9860

Conference

Conference20th IEEE Consumer Communications and Networking Conference, CCNC 2023
Country/TerritoryUnited States
CityLas Vegas
Period1/8/231/11/23

Keywords

  • Coverage
  • Disaster relief
  • Emergency networks
  • Reinforcement Learning
  • Trajectory optimization
  • UAV

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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