2019· International Journal of Machine Learning and Predictive Analytics· Vol 2, pp. 01-10· 0 citations
TL;DR
This paper explores various deployment models of edge AI tailored for real-time industrial automation feedback systems, and analyses on-device, edge gateway, and hybrid edge-cloud approaches, discussing their architectures, benefits, limitations, and real-world applicability.
Abstract
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.
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 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
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
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
The Fourth Industrial Revolution is accelerating the adoption of Industry 4.0 through intelligent computing, Industrial Internet of Things (IIoT), edge computing, and AI-driven automation. Traditional cloud-based industrial systems often experience latency, bandwidth limitations, network congestion, and privacy concerns, making them unsuitable for real-time manufacturing applications. This paper proposes an Edge Intelligence framework that integrates distributed edge computing, real-time AI analytics, and autonomous decision-making to process industrial IoT data locally. The architecture consists of four layers: perception, edge intelligence, autonomous decision, and cloud coordination. Industrial sensors, PLCs, and robotic systems collect operational data, while lightweight machine learning, deep learning, and reinforcement learning models perform feature extraction, anomaly detection, predictive analytics, and adaptive control with minimal latency. A mathematical optimization model minimizes processing delay, energy consumption, and resource utilization while maximizing accuracy and efficiency. Experimental evaluation demonstrates improved real-time performance, decision accuracy, fault detection, scalability, and resource utilization, making the framework well suited for next-generation smart manufacturing and sustainable industrial automation.
V. Sethi· International Journal of Int...· 0 citations
This paper presents a comprehensive study of edge-cloud orchestration strategies tailored for scalable industrial automation systems, and reveals that intelligent orchestration can significantly enhance operational efficiency, system scalability, and responsiveness in industrial settings.
A. Reza· International Journal of Mac...· 0 citations
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