Autonomous and Resilient Smart Infrastructure: An AI–Driven Cyber Physical Systems (CPS) Framework with Deep Reinforcement Learning and Blockchain Security
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.
Authors
- Pinamala Sruthi
- D. Suresh
- Manisha Bhimrao Mane
- N. Vijayakuma
- R. Arulmozhi
Publication Details
- Journal
- CompSci & AI Advances
- Published
- 2025-03-01
- DOI
- https://doi.org/10.69626/cai.2025.0084
- Primary Topic
- Smart Grid Security and Resilience
- Type
- article
- Field-Weighted Citation Impact
- 0.00