Aug 2026· International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies· 0 citations
TL;DR
The results of this study demonstrate that, when combined with hardware-based edge intelligence, AI-powered cloud analytics can significantly enhance the responsiveness, scalability, and decision accuracy in cyber-physical infrastructure systems.
Abstract
Real-time monitoring and smart decision-making are required in cyber-physical infrastructures such as smart grids, transportation systems, and industrial automation systems to ensure process efficiency and system resilience. However, higher latency, bandwidth constraints, and a lack of responsiveness to time-sensitive data streams haunt traditional cloud-based architectures. To address these challenges, a coherent framework for integrating AI-based cloud analytics with edge intelligent hardware for managing cyber-physical infrastructure is proposed in the following paper. The architecture is based on distributed edge nodes, with hardware accelerators for very low-latency inference, and cloud layers that perform large-scale analytics and optimisation of global models. A hybrid resource allocation strategy is a self-regulating plan for the allocation of computing workloads across cloud and edge environments. Also, an adaptive learning mechanism improves prediction accuracy in changing operational environments. The framework is mathematically designed to achieve optimal latency, throughput, and computational efficiency. The proposed system reduces latency by 145ms to 92 (≈36.5) units and increases prediction accuracy from 81.2% to 96.4% when applied to a dynamic workload. The convergence analysis shows that standardized quicker convergence occurs after 35 iterations as opposed to 60 iterations in the models at the baseline. Additionally, the throughput is proceeding at 520 requests to 780 requests and error rates are falling by a factor of around 41, ensuring better reliability. Relative performance analysis across various scenarios indicates consistent improvement in both edge-dominant and cloud-dominant setups. The results of this study demonstrate that, when combined with hardware-based edge intelligence, AI-powered cloud analytics can significantly enhance the responsiveness, scalability, and decision accuracy in cyber-physical infrastructure systems.
Due to the rapid development of cloud-based Cyber-Physical Systems (CPS) and smart hardware, a large volume of industrial data has been generated, posing challenges related to latency, scalability, and effective hardware-software integration. The paper provides a framework for an AI-enabled hardware-software co-design of real-time CPS data analytics. It is proposed that the smart sensor hardware, edge-assisted processing, and machine learning models are integrated into the proposed system to enable low-latency decision-making and optimal system performance. The co-design technique guarantees effective communication between hardware potential and software intelligence, reducing computational overhead and communication delays. The proposed framework is evaluated on 10,000 records, showing 91.3% prediction accuracy, 88.7% processing efficiency, 0.87% system stability, and a low computational latency of 198 ms. In addition, the general performance index is 0.89 and it demonstrates the balanced scaling, responsiveness and efficiency. The superiority demonstrated by comparative analysis over traditional, machine-learning-based, and hybrid models indicates that the proposed model is the best approach for real-time industrial analytics in dynamic CPS settings.
S. Maurya, R. Shekhar, Kavita Mandal· International Journal on Com...· 0 citations
The analysis indicates that effective edge-cloud AI systems require adaptive workload placement, privacy-preserving distributed learning, security-aware inference, explainability, fault tolerance, and continuous resource optimization rather than simple physical distribution of computation.
Amir Hosseini, L. Karimi· International Journal of Adv...· 0 citations
The growing use of cyber-physical systems (CPS) in business processes such as smart manufacturing, healthcare, transport, and energy has raised major concerns regarding real-time monitoring, control, scalability, and system trustworthiness. The established centralised cloud-based applications are characterised by low responsiveness and high latency (usually 4060 ms), whereas systems that are based solely on the edges do not have the intelligence and coordination across the globe. The following paper will propose a machine-learning-based system to monitor and control CPS in real time, with very low decision-making latency, and to perform scaled system intelligence using edge gateways and cloud networks. In the suggested scheme, edge gateways will process real-time data, identify anomalies, implement control measures using lightweight machine learning models, coordinate their operations globally, optimise their models, and conduct long-term analytics, all provided through the cloud layer. The system has been able to promote distributed learning without disseminating raw data, hence enhancing efficiency and privacy. The comparison of the proposed method, based on the experimental evaluation of over 100 monitoring cycles, shows that the necessary detection accuracy (approximately 91%), compared to the centralised (approximately 80%) and edge-only (approximately 72%) methods, is rather good. Also, response latency decreases to 21 ms from 48 ms, while communication overhead remains at about 85 MB per cycle, making deployment practical and scalable.
Harshita· International Journal on Eng...· 0 citations
Findings indicate that compression and knowledge distillation can reduce communication burdens, while heterogeneous aggregation and adaptive learning mechanisms improve the practicality of distributed AI environments.
Arif Setiawan, Maya Permata· International Journal of Com...· 0 citations
This paper explores hybrid cloud-edge infrastructures as a scalable solution for deploying AI in IIoT environments and presents an architectural framework that balances compute-intensive model training in the cloud with low-latency inference at the edge.
Jennifer Clark· International Journal of Mac...· 0 citations
The rapid advancement of Industry 4.0 has accelerated the development of intelligent and autonomous smart factories powered by Industrial Internet of Things (IIoT) devices, cyber-physical systems (CPS), and advanced manufacturing technologies. Although cloud computing offers significant computational and storage capabilities, it suffers from latency, bandwidth limitations, privacy concerns, and delayed decision-making in time-critical industrial environments. This paper proposes an Integrated Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations, combining edge computing, Artificial Intelligence (AI), digital twins, and predictive analytics to enable real-time local data processing with seamless cloud integration. The framework employs machine learning for anomaly detection, deep learning for automated quality inspection, reinforcement learning for adaptive production scheduling, and predictive models for equipment health monitoring. Secure communication protocols enhance data protection and system reliability. Experimental results demonstrate improved latency, prediction accuracy, manufacturing efficiency, energy utilization, fault detection, and operational resilience compared with conventional cloud-based approaches. The proposed architecture provides a scalable and sustainable solution for next-generation autonomous smart factories, improving equipment reliability, reducing operational costs, and increasing manufacturing productivity.
Seshagiri N· International Journal of Mod...· 0 citations
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