Autonomous and Resilient Smart Infrastructure: An AI–Driven Cyber Physical Systems (CPS) Framework with Deep Reinforcement Learning and Blockchain Security

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ABSTRACT

The convergence of Artificial Intelligence (AI) and Cyber-Physical Systems (CPS) is revolutionizing smart infrastructure by enabling autonomous decision-making, self-optimization, and resilience in dynamic environments. This research presents an advanced AI-infused CPS framework that integrates deep reinforcement learning (DRL), digital twin simulations, edge-cloud orchestration, and blockchain-based security to enhance adaptability, efficiency, and cybersecurity in smart infrastructure. Unlike conventional CPS architectures that rely on static rule-based control mechanisms, the proposed system employs self-learning algorithms, predictive analytics, and decentralized security protocols to autonomously detect anomalies, optimize resource allocation, and mitigate cyber threats in real-time. Experimental validation across smart grids, intelligent transportation, and industrial automation demonstrates significant improvements, including a 45% reduction in system failures, a 50% enhancement in operational efficiency, and a 35% increase in cyber resilience compared to traditional CPS models. The DRL-based decision-making model enables continuous policy refinement through environmental interactions, while digital twin technology facilitates predictive maintenance and risk assessment. Blockchain integration ensures tamper-proof data integrity and decentralized access control, addressing critical security vulnerabilities in centralized CPS architectures. Additionally, edge-cloud orchestration minimizes latency, enabling real-time AI inference and fault tolerance in bandwidth-constrained scenarios. This research contributes to the development of next-generation smart infrastructure by providing a scalable, secure, and adaptive AI-CPS framework. The findings highlight the transformative potential of AI-driven autonomy in critical infrastructure, paving the way for self-healing systems, explainable AI (XAI) integration, and quantum computing-enhanced optimizations in future CPS deployments.

Significance of the Study:

This study presents an AI-driven Cyber-Physical Systems (CPS) framework integrating deep reinforcement learning (DRL), digital twins, and blockchain security to revolutionize smart infrastructure. The system achieves 45% fewer failures, 50% higher efficiency, and 35% improved cyber resilience versus conventional CPS. DRL enables autonomous decision-making, while blockchain ensures tamper-proof data integrity. Edge-cloud orchestration reduces latency by 45%, enabling real-time AI inference. This research advances self-healing, secure smart infrastructure for grids, transportation, and Industry 4.0, paving the way for quantum-enhanced and explainable AI (XAI) in future CPS.

Summary of the Study:

The study proposes an AI-CPS framework combining DRL, digital twins, and blockchain for autonomous smart infrastructure. Validated in smart grids and transportation, it reduces failures by 45%, boosts efficiency by 50%, and enhances cyber resilience by 35%. DRL enables adaptive control, digital twins predict maintenance needs, and blockchain secures data. Edge-cloud integration cuts latency by 45%. Future work explores federated learning and quantum optimizations. This framework establishes self-optimizing, secure CPS for critical infrastructure, bridging AI and physical systems for a resilient, interconnected world.