AI-Enhanced Sensor Network Data for Predictive Maintenance
Author Information
Author(s): Sathupadi Kaushik, Achar Sandesh, Bhaskaran Shinoy Vengaramkode, Faruqui Nuruzzaman, Abdullah-Al-Wadud M., Uddin Jia
Primary Institution: King Saud University
Hypothesis
Can a hybrid edge-cloud framework improve predictive maintenance in sensor networks by reducing latency, energy consumption, and bandwidth usage?
Conclusion
The proposed hybrid edge-cloud framework significantly reduces latency by 35%, energy consumption by 28%, and bandwidth usage by 60% compared to cloud-only solutions.
Supporting Evidence
- The hybrid approach achieved a 35% reduction in latency compared to cloud-only solutions.
- Energy consumption decreased by 28% when using the hybrid framework.
- Bandwidth usage was reduced by 60% by processing data at the edge before sending it to the cloud.
- The framework is designed to optimize resource consumption in real-time predictive maintenance.
- Real-time anomaly detection was performed using a KNN model on edge devices.
- Failure predictions were made using an LSTM model hosted in the cloud.
- The study utilized a dataset of 123,013 instances for training and testing.
- The proposed system is applicable in resource-constrained environments.
Takeaway
This study shows that using both edge devices and cloud servers together can help machines predict when they need maintenance faster and use less energy.
Methodology
The study used a K-Nearest Neighbors (KNN) model for real-time anomaly detection on edge devices and a Long Short-Term Memory (LSTM) model in the cloud for predictive failure analysis.
Limitations
The framework lacks a mechanism for handling cloud server downtime and is limited by the computational capabilities of the edge devices.
Digital Object Identifier (DOI)
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