2020· International Journal of Machine Learning and Predictive Analytics· 0 citations
TL;DR
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.
Abstract
Industrial Internet of Things (IIoT) ecosystems are increasingly reliant on artificial intelligence (AI) to enable predictive analytics, real-time control, and intelligent automation. However, the centralized nature of traditional cloud computing introduces latency, bandwidth, and privacy constraints that limit the real-time applicability of AI models in industrial settings. This paper explores hybrid cloud-edge infrastructures as a scalable solution for deploying AI in IIoT environments. We present an architectural framework that balances compute-intensive model training in the cloud with low-latency inference at the edge. Key challenges including model orchestration, data management, and security in distributed environments are analyzed. Through case studies and performance evaluations, we demonstrate how hybrid architectures can effectively support scalable and resilient AI deployments for a range of industrial applications. Our findings highlight open research challenges and provide recommendations for building robust hybrid IIoT systems.
Edge AI is revolutionizing the industrial automation landscape by enabling real-time decision-making and feedback directly at the data source. Unlike traditional cloud-centric architectures, edge AI reduces latency, enhances data privacy, and ensures uninterrupted operations even in bandwidth-constrained environments. This paper explores various deployment models of edge AI tailored for real-time industrial automation feedback systems. We analyze on-device, edge gateway, and hybrid edge-cloud approaches, discussing their architectures, benefits, limitations, and real-world applicability. Through the lens of case studies and optimization techniques, we demonstrate how edge AI fosters responsiveness and resilience in smart industrial systems. The paper also outlines challenges and future research directions in deploying scalable, secure, and efficient edge AI solutions for Industry 4.0 and beyond.
Fatima Noor, S. Rahman· International Journal of Mac...· 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.
Dr. Amir Hosseini, dr.nematollah karimi· International Journal of Adv...· 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.
Doni Setiawan, Ditha Permata· International Journal of Com...· 0 citations
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.
Naveen, Satyam Kumar Sainy· International Journal on Eng...· 0 citations
A research-driven conceptual framework for resilient edge-to-cloud AI architectures supporting distributed real-time decision making and identifies limitations associated with heterogeneous devices, uncertain ground truth, model drift, communication failures, and the absence of uniform evaluation criteria are identified.
Chinedu Eze, Fatima Bello· International Journal of Adv...· 0 citations
A scalable edge-to-cloud AI inference pipeline in which inference tasks are dynamically distributed across heterogeneous edge and cloud resources is examined, providing a basis for resilient real-time AI systems while highlighting unresolved challenges involving heterogeneous hardware, dynamic workloads, privacy-utility trade-offs, and cross-layer optimization.
Dr. Khalid Al- Mansour· International Journal of Com...· 0 citations