The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAVs can also enhance the security of wireless networks. This paper defines the problem of eavesdropping on the link between the ground user and the UAV, which serves as an aerial base station (ABS). The reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power to enhance the data rate of the ground user. This increases the secrecy capacity without the system knowing the location of the eavesdropper. Simulation results show fast convergence and the highest secrecy capacity of the proposed DQN compared to Q-learning and two baseline approaches.
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Use a UAV System to Enhance Port Security in Unconstrained Environment
Ensuring maritime port security—a rapidly increasing concern in a post-9/11 world—presents certain operational challenges. As batteries and electric motors grow increasingly lighter and more powerful, unmanned aerial vehicles (UAVs) have been shown to be capable of enhancing a surveillance system’s capabilities and mitigating its vulnerabilities. In this paper, we looked at the current role unmanned systems are playing in port security and proposed an image-based method to enhance port security. The proposed method uses UAV real-time videos to detect and identify humans via human body detection and facial recognition. Experiments evaluated the system in real-time under differing environmental, daylight, and weather conditions. Three parameters were used to test feasibility: distance, height and angle. The findings suggest UAVs as an affordable, effective tool that may greatly enhance port safety and security.
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- Award ID(s):
- 1726500
- PAR ID:
- 10172493
- Date Published:
- Journal Name:
- International Conference on Applied Human Factors and Ergonomics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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