Electrical and Computer Engineering ETDs

Publication Date

Summer 7-29-2025

Abstract

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial Vehicles in dynamic settings, integrates disaggregated architectures with Multi-Access Edge Computing to enhance scalability in Non-Terrestrial Networks, and implements blockchain to secure satellite communications with decentralized trust. This work establishes a foundational paradigm for 6G technologies, promising transformative impacts across remote healthcare, smart cities, autonomous systems, and industrial automation, thereby redefining the future of wireless connectivity.

Keywords

Multi-Agent Systems (MAS), Intelligent Orchestration, Distributed Systems, Non Terrestrial Networks ( NTN), Edge Computing, Reinforcement Learning (RL)

Document Type

Dissertation

Language

English

Degree Name

Computer Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Michael Devetsikiotis

Second Committee Member

Christos Christodoulou

Third Committee Member

Ali Bidram

Fourth Committee Member

Claudio Sacchi

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