Smarter sampling, better efficiency
In industrial environments, real-time data acquisition is crucial for optimizing processes, ensuring safety, and enabling predictive analytics. However, high-frequency data sampling presents challenges in storage, transmission, and battery life for wireless sensors.Qubika’s Marcos Soto has tackled this problem in his latest research, published on arXiv, by developing smart sampling strategies that minimize aliasing errors while preserving key data trends for machine learning models.
His findings show that by applying mathematical optimization and compensation techniques, sampling rates can be reduced by up to 80% without sacrificing data quality.
Key takeaways:
- Reduced sampling frequency – Achieved a significant drop in data collection while maintaining accuracy.
- Extended battery life – Lower transmission frequency leads to 5x longer battery performance in wireless industrial sensors.
- Enhanced machine learning models – Optimized data acquisition improves trend detection and anomaly prediction.
- Cost-efficiency – Lower data storage and transmission needs, reducing operational expenses.
This research opens new doors for energy-efficient, intelligent industrial monitoring—a game-changer for oil and gas telemetry, predictive maintenance, and real-time decision-making.
🔎 Read the full research paper here: Smart Sampling Strategies for Wireless Industrial Data Acquisition
Want to learn more about Qubika’s capabilities in AI and embedded engineering? Check out our AI Studio and our Embedded Engineering Studio.
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