Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach

Noor Aboueleneen*, Abdulmalik Alwarafy, Mohamed Abdallah*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Internet of Drones (IoD)-aided wireless networks are proving their efficiency in various commercial and military applications, such as object recognition, surveillance, and data acquisition. However, the broadcast communication nature of IoD networks raises significant communication security issues. This paper investigates drone-to-ground communication subject to eavesdroppers in urban environments. We aim to provide secure communication utilizing physical layer security by increasing network secrecy rates. In addition, we aim to reduce the energy consumption within the IoD network by optimizing drones' transmitting and jamming power and employing energy harvesting techniques to charge drones wirelessly. Our optimization problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is proposed to solve the problem.

Original languageEnglish
Title of host publication2023 International Wireless Communications and Mobile Computing, IWCMC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages818-823
Number of pages6
ISBN (Electronic)9798350333398
DOIs
Publication statusPublished - 2023
Event19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 - Hybrid, Marrakesh, Morocco
Duration: 19 Jun 202323 Jun 2023

Publication series

Name2023 International Wireless Communications and Mobile Computing, IWCMC 2023

Conference

Conference19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023
Country/TerritoryMorocco
CityHybrid, Marrakesh
Period19/06/2323/06/23

Keywords

  • Internet of drones (IoD)
  • deep reinforcement learning
  • energy efficiency
  • security

Fingerprint

Dive into the research topics of 'Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach'. Together they form a unique fingerprint.
  • EX-QNRF-NPRPS-37: Secure Federated Edge Intelligence Framework for AI-driven 6G Applications

    Abdallah, M. M. (Lead Principal Investigator), Al Fuqaha, A. (Principal Investigator), Hamood, M. (Graduate Student), Aboueleneen, N. (Graduate Student), Student-1, G. (Graduate Student), Student-2, G. (Graduate Student), Fellow-1, P. D. (Post Doctoral Fellow), Assistant-1, R. (Research Assistant), Mohamed, D. A. (Principal Investigator), Mahmoud, D. M. (Principal Investigator), Al-Dhahir, P. N. (Principal Investigator) & Khattab, P. T. (Principal Investigator)

    19/04/2130/08/24

    Project: Applied Research

Cite this