Date of Award

5-12-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical Engineering and Computer Science

Advisor(s)

Mustafa Cenk Gursoy

Subject Categories

Computer Engineering | Engineering

Abstract

Unmanned Aerial Vehicles (UAVs) have become indispensable in a variety of fields, including surveillance, emergency response, packet delivery, and data collection for the Internet of Things (IoT). These systems are also critical in enhancing connectivity within cellular networks. This dissertation focuses on improving UAV operational efficiency and security by advancing trajectory optimization techniques, enhancing trajectory design through antenna radiation patterns, and increasing resilience against cyber-physical attacks, particularly GPS sensor faults. The first part of the study addresses UAV trajectory optimization in wireless communications, highlighting the significance of trajectory planning in improving the efficiency and reliability of UAV operations. Effective trajectory planning ensures optimal energy usage and maximized coverage areas, crucial for maintaining robust communication links with ground base stations (GBS). This optimization is vital for enhancing both the performance and safety of UAV operations in complex environments. Building on trajectory optimization, the second part of the research explores the impact of antenna radiation patterns on UAV trajectory design. The study develops an Enhanced Artificial Potential Field (Enhanced-APF) algorithm that integrates the 3D radiation patterns of antennas equipped on UAVs. This integration is essential for facilitating effective collision avoidance and smoother navigation paths, thereby optimizing UAV performance in scenarios involving multiple UAVs and ensuring safer operations across various applications. The final part of the dissertation introduces a novel algorithm, the Resilient Cyber-Attack Artificial Potential Field (RCA-APF), designed to enhance the resilience of UAV path planning against permanent GPS faults within a cyber-physical system (CPS) framework. This algorithm employs a three-stage process: detecting GPS faults due to the attack, estimating UAV location using Received Signal Strength (RSS) trilateration, and adjusting the UAV's path planning accordingly. The effectiveness of this approach is validated through rigorous experimental and simulation testing, demonstrating its capability to substantially improve the robustness of UAV operations against cyber-physical threats. Overall, this research provides comprehensive strategies for improving UAV trajectory planning and resilience, offering significant advancements in the safe and efficient deployment of UAVs. By integrating advanced cyber-security measures with strategic communications engineering, the dissertation contributes to the development of more reliable and effective UAV systems, paving the way for their expanded use in increasingly complex scenarios.

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Open Access

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