Edge Real-Time Medical Data Segmentation for IoT Devices with Computational and Memory Constrains

  • Marcin Bernas
  • Bartłomiej Płaczek
  • Alicja Sapek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)


The Internet of Things (IoT) becomes very important tool for data gathering and management in many environments. The majority of dedicated solutions register data only at time of events, while in case of medical data full records for long time periods are usually needed. The precision of acquired data and the amount of data sent by sensor-equipped IoT devices has vital impact on lifetime of these devices. In case of solutions, where multiple sensors are available for single device with limited computation power and memory, the complex compression or transformation methods cannot be applied - especially in case of nano device injected to a body. Thus this paper is focused on linear complexity segmentation algorithms that can be used by the resource-limited devices. The state-of-art data segmentation methods are analysed and adapted for simple IoT devices. Two segmentation algorithms are proposed and tested on a real-world dataset collected from a prototype of the IoT device.


Internet of Things Data segmentation Edge mining Medical data 


  1. 1.
    Charith, P., Chi, H., Srimal, J.: The emerging Internet of Things marketplace from an industrial perspective: a survey. IEEE Trans. Emerg. Top. Comput. 3(4), 34–42 (2015)Google Scholar
  2. 2.
    Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefGoogle Scholar
  3. 3.
    Athreya, A., Tague, P.: Network self-organization in the Internet of Things Networking and Control (IoT-NC). In: IEEE International Workshop, pp. 25–33 (2013)Google Scholar
  4. 4.
    Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., Leung, K.: A survey on the IETF protocol suite for the Internet of Things: standards, challenges, and opportunities. IEEE Wirel. Commun. 20(6), 91–98 (2013)CrossRefGoogle Scholar
  5. 5.
    Ning, H., Liu, H., Yang, L.: Cyberentity security in the Internet of Things. Computer 46(4), 46–53 (2013)CrossRefGoogle Scholar
  6. 6.
    Wesołowski, T.E., Porwik, P., Doroz, R.: Electronic health record security based on ensemble classification of keystroke dynamics. Appl. Artif. Intell. 30(6), 521–540 (2016)CrossRefGoogle Scholar
  7. 7.
    Tsai, C., Lai, C., Chiang, M., Yang, L.: Data mining for Internet of Things: a survey. Commun. Surv. Tutor. 99, 1–21 (2013)Google Scholar
  8. 8.
    Gaura, E., Brusey, J., Allen, M., Wilkins, R., Goldsmith, D., Rednic, R.: Edge mining the Internet of Things. IEEE Sens. J. 13(10), 3816–3825 (2013)CrossRefGoogle Scholar
  9. 9.
    Keogh, E., Chakrabarti, K., Pazzani, M., et al.: Knowl. Inf. Syst. 3, 263 (2001). doi: 10.1007/PL00011669CrossRefGoogle Scholar
  10. 10.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006). doi: 10.1007/978-1-4615-7566-5CrossRefzbMATHGoogle Scholar
  11. 11.
    Murakami, T., Asai, K., Yamazaki, E.: Vector quantiser of video signals. Electron. Lett. 18, 1005–1006 (1981)CrossRefGoogle Scholar
  12. 12.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the ACM SIGMOD, San Diego, CA, USA, pp. 9–12 (2003)Google Scholar
  13. 13.
    Ji, S., Xue, Y., Carin, L.: Bayesian compressive sensing. IEEE Trans. Signal Process. 56, 2346–2356 (2008)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Bello, J.: Measuring structural similarity in music. IEEE Trans. Audio Speech Lang. Process 19, 2013–2025 (2011)CrossRefGoogle Scholar
  15. 15.
    Schoellhammer, T., Greenstein, B., Osterweil, E., Wimbrow, M., Estrin, D.: Lightweight temporal compression of microclimate datasets. In: Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks, Tampa, FL, USA, 16–18 November 2004, pp. 516–524 (2004)Google Scholar
  16. 16.
    Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd Workshop on Temporal Data Mining, Edmonton, Canada, 23–26 July 2002, pp. 53–68 (2002)Google Scholar
  17. 17.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993). doi: 10.1007/3-540-57301-1_5CrossRefGoogle Scholar
  18. 18.
    Hunter, J., McIntosh, N.: Knowledge-based event detection in complex time series data. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds.) AIMDM 1999. LNCS, vol. 1620, pp. 271–280. Springer, Heidelberg (1999). doi: 10.1007/3-540-48720-4_30CrossRefGoogle Scholar
  19. 19.
    Kudłacik, P., Porwik, P., Wesołowski, T.: Fuzzy approach for intrusion detection based on user’s commands. Soft Comput. 20(7), 2705–2719 (2016)CrossRefGoogle Scholar
  20. 20.
    Balasubramaniam, S., Kangasharju, J.: Realizing the internet of nano things: challenges, solutions, and applications. Soft. Comput. 46(2), 62–68 (2013)Google Scholar
  21. 21.
    Danieletto, M., Bui, N., Zorzi, M.: A compression and classification solution for the Internet of Things. Sensors 14(1), 68–94 (2014). doi: 10.3390/s140100068CrossRefGoogle Scholar
  22. 22.
    Bernas, M., Płaczek, B.: Period-aware local modelling and data selection for time series prediction. Expert Syst. Appl. 59, 60–77 (2016)CrossRefGoogle Scholar
  23. 23.
    Preacher, K., Curran, P., Bauer, D.: Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. J. Educ. Behav. Stat. 31(4), 437–448 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marcin Bernas
    • 1
  • Bartłomiej Płaczek
    • 1
  • Alicja Sapek
    • 1
  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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