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.

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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
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article

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

Pinamala Sruthi, D. Suresh, Manisha Bhimrao Mane, N. Vijayakuma et al.
CompSci & AI Advances
Smart Grid Security and Resilience
article

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

Pinamala Sruthi, D. Suresh, Manisha Bhimrao Mane, N. Vijayakuma, R. Arulmozhi
article en

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.

CompSci & AI AdvancesVol. 02(01)
Openalex Percentile: Top 68%
Smart Grid Security and Resilience
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Autonomous and Resilient Smart Infrastructure: An AI–Driven Cyber Physical Systems (CPS) Framework with Deep Reinforcement Learning and Blockchain Security — Pinamala Sruthi, D. Suresh, et al. · CompSci & AI Advances (2025) | TGRS Research Map | TGRS