Edge AI for Real-Time Decision Making: A Low-Latency Approach for Industrial Automation
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Abstract
The rapid expansion of IoT and Industry 4.0 has increased the demand for low-latency AI-driven decision-making at the edge. This paper explores the integration of artificial intelligence with edge computing, enabling real-time analytics for industrial automation, predictive maintenance, and robotics. We propose an optimized edge AI framework that leverages lightweight deep learning models and hardware acceleration techniques to enhance processing efficiency. Experimental results demonstrate significant improvements in latency, energy consumption, and real-time adaptability compared to cloud-based AI solutions. The study also addresses challenges related to model deployment, security, and interoperability in edge AI environments.
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References
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