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Lossless Time Series Data Compression Technique Applicable to Edge Nodes

에지노드에 적용 가능한 무손실 시계열데이터 압축 기법

  • Soosung Lee (GITC.) ;
  • Sang-Ho Hwang (GITC.) ;
  • Sungho Kim (GITC.) ;
  • Jang-Kyu Yun (GITC.) ;
  • Yong-Wan Park (Yeungnam University)
  • 이수성 ;
  • 황상호 ;
  • 김성호 ;
  • 윤장규 ;
  • 박용완
  • Received : 2024.05.29
  • Accepted : 2024.08.12
  • Published : 2024.08.31

Abstract

In this paper, we propose an improved technique called HAB (Huffman encoding Aware Bitpacking) that enhances the Sprintz method, which is a representative time series data compression technique. The proposed technique boosts compression rates by incorporating Huffman encoding-aware bitpacking in the secondary compression stage. Additionally, it can be applied to terminal nodes or edge nodes with limited available resources, as it does not require separate parameters or storage space for compression. The proposed technique is a lossless method and is suitable for fields that require the generation of artificial intelligence models and accurate data analysis. In the experimental evaluation, the proposed HAB showed an average improvement of 14.7% compared to the existing technique in terms of compression rate.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음. (22AMDP-C162334-02 : 자동차 통합보안 안전성 평가기술 개발)

References

  1. M. R. Chowdhury, S. Tripathi, S. De, "Adaptive Multivariate Data Compression in Smart Metering Internet of Things," IEEE Transactions on Industrial Informatics, Vol. 17, No. 2, pp. 1287-1297, 2020.
  2. R. Krishnamurthi, A. Kumar, D. Gopinathan, A. Nayyar, B Qureshi, "An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques," Sensors, Vol 20, No. 21, 6076, 2020.
  3. G. Campobello, A. Segreto, S. Zanafi, S. Serrano, "RAKE: A Simple and Efficient Lossless Compression Algorithm for the Internet of Things," 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2581-2585, 2017.
  4. S. K. Jensen, T. B. Pedersen, C. Thomsen, "Time Series Management Systems: a 2022 Survey," Data Series Management and Analytics. Association for Computing Machinery, 2022.
  5. K. Chirikhin, B. Ryabko, "Compression-based Methods of Time Series Forecasting," Mathematics, Vol. 9, No. 3, pp. 384, 2021.
  6. S. H. Hwang, K. M. Kim, S. Kim, J. W. Kwak, "Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing," IEMEK Journal of Embedded Systems and Applications, Vol. 18, No. 1, pp. 1-7, 2023.
  7. G. Chiarot, C. Silvestri, "Time Series Compression Survey," ACM Computing Surveys (CSUR), Vol. 55, No. 10, pp. 1-32, 2022.
  8. M. A. de Oliveira, A. M. da Rocha, F. E. Puntel, G. G. H. Cavalheiro, "Time Series Compression for IoT: A Systematic Literature Review," Wireless Communications and Mobile Computing 2023, 5025255, 2023.
  9. R. Akhter, S. A. Sofi, "Precision Agriculture Using IoT Data Analytics and Machine Learning," Journal of King Saud University-Computer and Information Sciences, Vol. 34, No. 8, pp. 5602-5618, 2021.
  10. C. J. Deepu, C. H. Heng, Y. Lian, "A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT," IEEE Transactions on Biomedical Circuits and Systems, Vol. 1, No. 2, pp. 245-254, 2016.
  11. L. Yan, J. Han, R. Xu, Z. Li, "Model-free Lossless Data Compression for Real-time Low-latency Transmission in Smart Grids," IEEE Transactions on Smart Grid, Vol. 12, No. 3, pp. 2601-2610, 2020.
  12. A. K. Idrees, S. K. Idrees, R. Couturier, T. Ali-Yahiya, "An Edge-fog Computing-enabled Lossless EEG Data Compression with Epileptic Seizure Detection in IoMT Networks," IEEE Internet of Things Journal, Vol. 9, No. 15, pp. 13327-13337, 2022.
  13. B. Barbarioli, G. Mersy, S. Sintos, S. Krishnan, "Hierarchical Residual Encoding for Multiresolution Time Series Compression," Proceedings of the ACM on Management of Data Vol. 1, No. 1, pp. 1-26, 2023.
  14. A. Bruno, F. M. Nardini, G. E. Pibiri, R. Trani, R. Venturini, "Tsxor: A Simple Time Series Compression Algorithm," SPIRE 2021: String Processing and Information Retrieval, pp 217-223, 2021.
  15. T. Pelkonen, S. Franklin, J. Teller, P. Cavallaro, Q. Huang, J. Meza, K. Veeraraghavan, "Gorilla: A Fast, Scalable, In-memory Time Series Database," Proceedings of the VLDB Endowment, Vol. 8, No. 12, pp. 1816-1827, 2015.
  16. D. Blalock, S. Madden, J. Guttag, "Sprintz: Time Series Compression for the Internet of Things," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 2, No. 3, pp 1-23, 2018.
  17. J. Burgues, J. M. Jimenez-Soto, S. Marco, "Estimation of the Limit of Detection in Semiconductor Gas Sensors Through Linearized Calibration Models," Analytica Chimica Acta, Vol. 1013, pp. 13-25, 2018.
  18. L. M. Candanedo, V. Feldheim, D. Deramaix, "Data Driven Prediction Models of Energy Use of Appliances in a Low-energy House," Energy and Buildings, Vol. 140, pp. 81-97, 2017.