Blockchain-based secure Unmanned Aerial Vehicles (UAV) in network design and optimization

Hafeez, Sana (2024) Blockchain-based secure Unmanned Aerial Vehicles (UAV) in network design and optimization. PhD thesis, University of Glasgow.

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as transformative technologies with wide ranging applications, including surveillance, mapping, remote sensing, search and rescue, and disaster management. As sophisticated Unmanned Aerial Vehicle (UAV) increasingly operate in collaborative swarms, joint optimization challenges arise, such as flight trajectories, scheduling, altitude, Aerial Base Stations (ABS), energy harvesting, power transfer, resource allocation, and power consumption. However, the widespread adoption of UAV networks has been hindered by challenges related to optimal Three-Dimensional (3D) deployment, trajectory optimization, wireless and computational resource allocation, and limited flight durations when operating as ABSs. Crucially, the broadcast nature of UAV-assisted wireless networks renders them susceptible to privacy and security threats such as Distributed Denial-of-Service (DDoS) replay, impersonation, message injection, spoofing, malware infection, eavesdropping, and line of-interference attacks.

This study aims to address these privacy and security challenges by leveraging blockchain technology’s potential to secure data and delivery in UAV communication networks. With amalgamation of blockchain, this study seeks to harness its inherent immutability and cryptographic properties to ensure secure and tamper-proof data transmission, promote trust and transparency among stakeholders, enable automated Smart Contract (SC) for secure delivery, and facilitate standardization and interoperability across platforms. Specifically, blockchain can secure UAV network privacy and security through data privacy and integrity, secure delivery and tracking, access control, identity management, and resilience against cyber-attacks.

Furthermore, this study explores the synergies among blockchain, UAV networks, and Federated Learning (FL) for privacy-preserving intelligent applications in healthcare and wireless networks. FL enables collaborative training of Machine Learning (ML) models without sharing raw data, ensuring data privacy. By integrating FL with blockchain-assisted UAV networks, this study aims to revolutionize future intelligent applications, particularly in time-sensitive and privacy-critical domains. Overall, this thesis contributes to the field by providing a comprehensive analysis of integrating blockchain, FL, and UAV networks, beyond Fifth-Generation (5G) communication networks. It addresses privacy and security concerns related to data and delivery, thereby enabling secure, reliable, and intelligent applications in various sectors.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Blockchain, drone communication, authentication, federated learning, privacy, security, UAV networks, data integrity, secure delivery.
• Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Sun, Dr. Yao, Mohjazi, Dr. Lina and Imran, Professor Muhammad Ali
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84460
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 12 Jul 2024 15:07
• Last Modified: 12 Jul 2024 15:12
• Thesis DOI: 10.5525/gla.thesis.84460
• URI: https://theses.gla.ac.uk/id/eprint/84460

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