Electrical and Computer Engineering ETDs

Publication Date

Spring 5-16-2026

Abstract

This Ph.D. dissertation presents a unified Hierarchical Safe Reinforcement Learning (HSRL) framework for mission-aware, edge-enabled multi-UAV Internet of Things (IoT) networks. The work addresses the need for autonomous aerial infrastructures capable of delivering low-latency communication, scalable edge computation, and provably safe operation in dynamic environments. The dissertation develops three primary contributions. First, it formulates longhorizon drone base station placement and load balancing as a strategic actor–critic learning problem, enabling proactive adaptation to spatiotemporal demand variations. Second, it introduces a mission-aware multi-agent reinforcement learning controller for coordinated mobility, sensing, and computation offloading under latency and energy constraints. Third, it integrates a distributed control barrier function safety layer that guarantees collision avoidance and forward invariance during both training and deployment. Overall, this work establishes a scalable, safety-certified foundation for resilient multi- UAV autonomy in next-generation 6G-enabled IoT systems.

Keywords

Hierarchical reinforcement learning, multi-UAV systems, edge-enabled IoT networks, control barrier functions, mission-aware resource allocation, safe autonomous systems

Project Sponsors

None

Document Type

Dissertation

Language

English

Degree Name

Computer Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Dr. Michael Devetsikiotis

Second Committee Member

Dr. Rafael Fierro

Third Committee Member

Dr. Ramiro Jordan

Fourth Committee Member

Dr. Fernando Moreu

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