Governance and Optimization models for Autonomous and Smart Urban Systems: A Blockchain-Enabled and Learning-Based Approach

Contemporary cyber-physical systems operating within urban infrastructures confront three fundamental challenges: establishing distributed trust mechanisms absent centralized authorities, coordinating heterogeneous autonomous agents under uncertainty, and optimizing resource allocation subject to competing stakeholder objectives. The reconciliation of mathematical optimality with operational constraints while maintaining cryptographically verifiable guarantees across heterogeneous ecosystems constitutes a critical research gap. This dissertation develops and validates a unified methodological framework integrating distributed ledger technologies with machine learning paradigms and mathematical optimization to address these challenges systematically across autonomous vehicle coordination, intelligent building energy management, and organizational resource allocation domains. The proposed framework instantiates a three-layer architectural pattern that decomposes cyber-physical control into cryptographic trust establishment, intelligent decision synthesis, and domain-specific integration strata. The trust layer transforms local observations into globally verifiable intelligence through deterministic identification schemes. The intelligence layer implements domain-adapted optimization strategies comprising Deep Reinforcement Learning, Model Predictive Control augmented with Long Short-Term Memory networks, and Integer Linear Programming formulations. The integration layer provides semantic translation between mathematical abstractions and deployment-specific constraints through standardized interfaces maintaining backward compatibility with legacy systems. The cryptographic foundation employs blockchain-based consensus mechanisms with dynamic threshold adaptation that modulates validation requirements according to event severity, temporal urgency, and proposer reputation. The mathematical formulation of a dynamic threshold enables rapid dissemination of critical infrastructure updates through economic incentive alignment. Smart contract architectures execute on Ethereum Virtual Machine compatible platforms, demonstrating gas-efficient operations through optimized storage patterns and event-based logging mechanisms that reduce on-chain footprint by maintaining only cryptographic commitments rather than complete datasets. Experimental validation across three deployments substantiates the framework's efficacy and cross-domain transferability. The autonomous vehicle coordination system reduces training convergence time while demonstrating superior mean rewards relative to monolithic baselines through hierarchical action space factorization across four urban zones. The building automation implementation achieves reduction in energy consumption while maintaining thermal comfort within regulatory bounds through blockchain-notarized K-means clustering for occupancy pattern classification integrated with lexicographic optimization. The organizational allocation system generates optimal solutions for competing minimax fairness and team cohesion objectives, enabling practitioners to navigate trade-offs through continuous parametrization of weighting coefficient. This research establishes that distributed intelligence in cyber-physical systems emerges through systematic integration of cryptographic immutability providing mathematical certainty at the data layer, economic incentive structures aligning individual utility with collective optimum discovery, and algorithmic sophistication matching computational strategies to problem characteristics. The limitations identified include computational overhead of blockchain operations in resource-constrained environments, sample complexity requirements for Deep Reinforcement Learning convergence, and challenges in maintaining model accuracy when system dynamics evolve beyond training distributions. These constraints delineate boundaries for framework applicability while suggesting extensions through layer-two scaling solutions, transfer learning mechanisms, and robust optimization formulations that maintain performance despite modeling uncertainties. The validated methodological framework provides principled foundations for a trustworthy system design, demonstrating that reliable distributed coordination emerges from architectural coherence rather than technological innovation in isolation.