Edge AI Deployment Models for Real-Time Industrial Automation Feedback
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.