Constructing a Secure and Autonomous Infrastructure for Smart Cities
| dc.contributor.advisor | Lin, Dan | |
| dc.contributor.committeeChair | Lin, Dan | |
| dc.creator | Yu, Alian | |
| dc.creator.orcid | 0000-0001-7385-9551 | |
| dc.date.accessioned | 2025-09-26T11:10:16Z | |
| dc.date.created | 2025-08 | |
| dc.date.issued | 2025-07-09 | |
| dc.date.submitted | August 2025 | |
| dc.description.abstract | The rapid evolution of urban environments, propelled by advances in the Internet of Things (IoT) and autonomous vehicle technologies, has ushered in a new era of smart city infrastructure. This dissertation investigates four critical components that together form the backbone of a secure, efficient, and resilient urban digital ecosystem. First, this dissertation develops a flexible access control technique for large-scale public IoT services to manage vast, interconnected networks securely, ensuring data integrity and user privacy. Second, this dissertation proposes an AI-enabled efficient traffic scheduling for autonomous vehicles, which uses real-time decision-making to optimize routes, reduce congestion, and improve urban mobility. Third, to mitigate the risks of data exposure in centralized systems, this work extends to a privacy-preserving model that trains locally while sharing statistical traffic data for global optimization, ensuring secure and efficient decentralized traffic management. Finally, this dissertation proposes a neighborhood watch mechanism for attack detection and evacuation, which enhances intersection security by monitoring data for anomalies and triggering safety protocols to prevent cyber threats and system failures. These integrated solutions address critical challenges in modern urban environments. This dissertation is organized around the synergistic integration of these research areas. By establishing a secure IoT framework, enhancing autonomous traffic scheduling through AI, provide scalable and privacy-preserving traffic optimization, and implementing robust intersection safety measures, this dissertation provides a holistic solution to the challenges facing modern smart cities and illustrates how a multi-faceted approach can drive forward the next generation of urban infrastructure. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/1803/19889 | |
| dc.language.iso | en | |
| dc.subject | Autonomous Vehicle | |
| dc.subject | Intersection Management Unit | |
| dc.subject | Access Control | |
| dc.subject | Internet of Things | |
| dc.subject | Internet of Vehicles | |
| dc.subject | Road Safety | |
| dc.subject | Deep Learning | |
| dc.subject | Motion Planning | |
| dc.subject | Privacy-Preservation | |
| dc.title | Constructing a Secure and Autonomous Infrastructure for Smart Cities | |
| dc.type | Thesis | |
| dc.type.material | text | |
| local.embargo.lift | 2026-08-01 | |
| local.embargo.terms | 2026-08-01 | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Vanderbilt University Graduate School | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | PhD |