Abstract
Compression of telemetry streams is fundamental for both their storage and transmission. Recently, machine learning has been employed to enhance traditional data compression algorithms specifically for telemetry compression, both lossless and lossy. However, state-of-the-art telemetry compression algorithms are usually tailored to work with very specific datasets and can hardly generalize to different datasets. Moreover, much simpler traditional algorithms can often obtain better compression ratios with less computational complexity. In this work, we attempt a preliminary experiment aiming to verify the effectiveness of one of the most representative AI-based lossless telemetry compression algorithms against three different NASA datasets. Experimental results show that the model still struggles to perform better than traditional approaches, highlighting the necessity to design and study more sophisticated machine learning models for telemetry compression with a broader applicability.
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Notes
- 1.
The original dataset download page was taken down and NASA is currently in the process of putting data back online (https://www.nasa.gov/content/prognostics-center-of-excellence-data-set-repository). We were only able to retrieve data from 12 batteries.
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Ciaparrone, G., Benedetto, V., Gissi, F. (2023). A Preliminary Study on AI for Telemetry Data Compression. In: Troiano, L., Vaccaro, A., Kesswani, N., Díaz Rodriguez, I., Brigui, I., Pastor-Escuredo, D. (eds) Key Digital Trends in Artificial Intelligence and Robotics. ICDLAIR 2022. Lecture Notes in Networks and Systems, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-30396-8_12
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