Intelligent Edge Computing Architecture for Low-Latency AI Processing in IoT Networks
Abstract
This study introduces an intelligent edge computing architecture optimized for low-latency AI inference across large-scale IoT networks. The proposed system uses lightweight neural models, adaptive load distribution, and on-device caching to minimize communication overhead and improve real-time processing. Evaluations on smart city and industrial IoT testbeds show significant improvements in response time, bandwidth efficiency, and system scalability. The architecture provides a practical foundation for deploying AI capabilities directly at the edge, enabling faster, more reliable IoT intelligence.
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