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Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework.

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  • 1Google LLC, Sunnyvale, CA 94089, USA.

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Summary
This summary is machine-generated.

This study introduces an edge-cloud hybrid framework for real-time predictive maintenance, significantly reducing latency, energy use, and bandwidth needs for sensor networks.

Keywords:
K-nearest neighbors (KNN)bandwidth reductiondynamic workload managementenergy efficiencyhybrid edge-cloud frameworklatency optimizationlong short-term memory (LSTM) networkpredictive maintenancesensor networksensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Industrial Engineering

Background:

  • Sensor networks generate large real-time data volumes, straining traditional predictive maintenance due to latency, energy, and bandwidth constraints.
  • Existing cloud-only frameworks struggle with the demands of high-velocity data processing in industrial environments.

Purpose of the Study:

  • To propose and evaluate an edge-cloud hybrid framework for efficient real-time predictive maintenance.
  • To address the limitations of latency, energy consumption, and bandwidth in sensor network data analysis.

Main Methods:

  • Implemented a K-Nearest Neighbors (KNNs) model on edge devices for real-time anomaly detection.
  • Utilized a Long Short-Term Memory (LSTM) model in the cloud for in-depth time-series failure prediction.
  • Developed a dynamic workload management algorithm to optimize resource distribution between edge and cloud.

Main Results:

  • Achieved a 35% reduction in latency compared to cloud-only solutions.
  • Demonstrated a 28% decrease in energy consumption.
  • Reduced bandwidth usage by 60%.

Conclusions:

  • The proposed edge-cloud hybrid framework offers a scalable and efficient solution for real-time predictive maintenance.
  • This approach is highly suitable for resource-constrained, data-intensive environments.
  • Optimized task distribution enhances operational efficiency and maintenance scheduling.