Projects per year
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 language | English |
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Title of host publication | 2023 International Wireless Communications and Mobile Computing, IWCMC 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 818-823 |
Number of pages | 6 |
ISBN (Electronic) | 9798350333398 |
DOIs | |
Publication status | Published - 2023 |
Event | 19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 - Hybrid, Marrakesh, Morocco Duration: 19 Jun 2023 → 23 Jun 2023 |
Publication series
Name | 2023 International Wireless Communications and Mobile Computing, IWCMC 2023 |
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Conference
Conference | 19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 |
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Country/Territory | Morocco |
City | Hybrid, Marrakesh |
Period | 19/06/23 → 23/06/23 |
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.Projects
- 1 Finished
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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)
19/04/21 → 30/08/24
Project: Applied Research