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Adaptive Segmentation of Streaming Sensor Data on Edge Devices
Roman Dębski1, Rafał Dreżewski1
1Institute of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland.
Sensors (Basel, Switzerland)
|October 26, 2021
Summary
This study introduces an adaptive streaming algorithm for segmenting sensor data. The novel cubic splinelet-based method achieves significant data compression while maintaining high signal approximation accuracy in real-time.
Area of Science:
- Signal Processing
- Data Compression
- Time Series Analysis
Background:
- Sensor data streams often exhibit twice-differentiable properties, requiring specialized segmentation methods.
- Existing algorithms may not efficiently handle real-time segmentation, smoothing, and compression simultaneously.
Purpose of the Study:
- To develop an adaptive streaming algorithm for segmenting twice-differentiable sensor data streams.
- To enable real-time simultaneous segmentation, smoothing, and compression of sensor data.
Main Methods:
- The algorithm employs a greedy look-ahead strategy.
- It is built upon the concept of a cubic splinelet for signal representation.
- Segmentation quality is evaluated by approximation accuracy and compression ratio.
Main Results:
- Achieved compression ratios ranging from 135 to 208, reducing stream sizes significantly.
- Maintained approximation errors comparable to state-of-the-art global algorithms.
- Demonstrated simultaneous real-time segmentation, smoothing, and compression.
Conclusions:
- The proposed algorithm effectively segments, smooths, and compresses twice-differentiable sensor data streams in real-time.
- It offers a practical solution for applications like IoT analytics and embedded time-series databases.
- The method balances high compression with low approximation error.
