Enhancing Edge Computing Performance Using AI and ML
Enhancing Edge Computing Performance Using AI and ML
Mrs.D.A.Shinde1, Mrs P.R.Nagansure 2
1 Electronics and Telecommunication Department , S.E.S.Polytechnic,Solapur
2 Electronics and Telecommunication Department , S.E.S.Polytechnic,Solapur
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Abstract -.
Edge intelligence empowers smart metering infrastructure to execute localized data processing directly on field-level hardware, mitigating the need to route all telemetry to a centralized cloud. By running computational workloads on resource-constrained edge nodes (such as the ESP32), the architecture significantly reduces propagation delays, minimizes backhaul bandwidth utilization, and facilitates near-instantaneous localized decision-making. Synthesizing the Internet of Things (IoT) with Artificial Intelligence and Machine Learning (AI/ML) paradigms optimizes anomaly detection, load forecasting, real-time telemetry observation, and the overall operational efficiency of intelligent power grids.
Key Words: Internet of Things (IoT), Decentralized Architecture, Intelligent Metering, Artificial Intelligence (AI), Machine Learning (ML), Edge AI, Predictive Diagnostics, Smart Grid, Energy Analytics, Real-Time Processing.