Edge Computing and AI for Real-Time Industrial Automation
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Abstract
The increasing demand for low-latency processing in industrial environments has led to the adoption of edge computing integrated with AI. This paper proposes an edge-based AI framework for real-time monitoring and automation in smart factories. By processing data locally, the system reduces latency and improves response times. Machine learning models are deployed at the edge to detect anomalies and optimize operations. Experimental results indicate improved efficiency, reduced downtime, and enhanced scalability in industrial automation systems.
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References
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