Abstract
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.
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References
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)
Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
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)
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)
Ning, H., Liu, H., Yang, L.: Cyberentity security in the Internet of Things. Computer 46(4), 46–53 (2013)
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)
Tsai, C., Lai, C., Chiang, M., Yang, L.: Data mining for Internet of Things: a survey. Commun. Surv. Tutor. 99, 1–21 (2013)
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)
Keogh, E., Chakrabarti, K., Pazzani, M., et al.: Knowl. Inf. Syst. 3, 263 (2001). doi:10.1007/PL00011669
Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006). doi:10.1007/978-1-4615-7566-5
Murakami, T., Asai, K., Yamazaki, E.: Vector quantiser of video signals. Electron. Lett. 18, 1005–1006 (1981)
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)
Ji, S., Xue, Y., Carin, L.: Bayesian compressive sensing. IEEE Trans. Signal Process. 56, 2346–2356 (2008)
Bello, J.: Measuring structural similarity in music. IEEE Trans. Audio Speech Lang. Process 19, 2013–2025 (2011)
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)
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)
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_5
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_30
Kudłacik, P., Porwik, P., Wesołowski, T.: Fuzzy approach for intrusion detection based on user’s commands. Soft Comput. 20(7), 2705–2719 (2016)
Balasubramaniam, S., Kangasharju, J.: Realizing the internet of nano things: challenges, solutions, and applications. Soft. Comput. 46(2), 62–68 (2013)
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/s140100068
Bernas, M., Płaczek, B.: Period-aware local modelling and data selection for time series prediction. Expert Syst. Appl. 59, 60–77 (2016)
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)
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Bernas, M., Płaczek, B., Sapek, A. (2017). Edge Real-Time Medical Data Segmentation for IoT Devices with Computational and Memory Constrains. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_12
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