UAV networks improve security and energy use with deep learning
Efficient Joint Trajectory Optimization and Resource Allocation for Secure UAV Networks: A Deep Reinforcement Learning Approach
Information Theory
Summary
UAVs or drones can help send data wirelessly, but making sure these networks are secure and energy-efficient is hard. The authors study a system where drones can change their roles and gather energy while avoiding eavesdropping attacks. They use a type of AI called deep reinforcement learning to find the best flight paths and resource use to keep communications safe while saving energy. Their simulations show this method works better than older or simpler AI methods at protecting data and reducing energy use.
What this means in practice
- •For wireless network operators: Optimize drone flight paths and resource use for secure data delivery amid active eavesdropping threats.
- •For emergency response teams: Improve secure and efficient communication in drone networks during disaster recovery where ground infrastructure is compromised.
Authors
Abdulmalik Alwarafy, Mohamed Hayajneh, Mustafa Al Juboori, Abdulaziz Almarzooqi, Tahnoon Almazrouei, Robera Abera, Kaleab Nega
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in wireless networks; however, ensuring secure communication and efficient energy usage remains challenging. This paper addresses the joint trajectory optimization and resource allocation problem in UAV networks in the presence of a tethered UAV flying center and a malicious ground eavesdropper. In the proposed model, UAVs are equipped with energy harvesting capabilities and can adaptively switch between transmitting, jamming, and relaying roles to deliver data to a tethered UAV center for optimized network performance. An optimization problem is formulated to maximize a utility function that balances secrecy rate, energy consumption, and interference. To address the high non-convexity of the problem and its real-time decision requirements, a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm is proposed to solve the problem and learn the network dynamics. Extensive simulation results demonstrate that the proposed approach achieves superior secrecy performance, lower energy consumption, and reduced interference compared to conventional and DRL-based methods.