A Method of IoT Information Compression

Authors

  • Yuriy S. Manzhos
  • Yevheniia V. Sokolova

DOI:

https://doi.org/10.47839/ijc.21.1.2523

Keywords:

data approximation, Internet of Things, general orthogonal polynomials, lossy signal compression, modification of Chebyshev discrete transformation

Abstract

The Internet of Things (IoT) is a modern paradigm that consists of heterogeneous intercommunicated devices that send and receive messages in various formats through different protocols. Thanks to the smart things mainstream, it is becoming common to collect large quantities of data generated by resource-constrained, distributed devices at one or more servers. However, the wireless transmitting of data is very expensive. For example, in IoT, Bluetooth Low Energy using costs tens of millijoules per connection, while computing at full energy costs only tens of micrjoules, and sitting idle costs close to 1 microjoules per second for STM processors. We need compression of data on smart devices. We introduce an IoT compression method based on the concurrent Cosine and Chebyshev Discrete Transforms. For performance increasing, the modification of Transforms algorithms is proposed. This method is suitable not only for IoT devices collecting data but also for the big servers.

References

L. S. Bai, R. P. Dick, and P. A. Dinda, “Archetype-based design: Sensor network programming for application experts, not just programming experts,” in Proceedings of the 2009 IEEE International Conference on Information Processing in Sensor Networks, 2009, pp. 85–96.

Internet of Things – number of connected devices worldwide 2015–2025, [Online]. Available at: http://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide

D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of Things: vision, applications and research challenges,” Ad Hoc Networks, vol. 10, issue 7, pp. 1497–1516, 2012. https://doi.org/10.1016/j.adhoc.2012.02.016.

M. Buevich, A. Wright, R. Sargent, and A. Rowe, “Respawn: A distributed multi-resolution time-series datastore,” Proceedings of the 2013 IEEE 34th Real-Time Systems Symposium (RTSS), 2013, pp. 288–297. https://doi.org/10.1109/RTSS.2013.36.

D. Blalock, S. Madden, J. Guttag, “Sprintz: Time series compression for the Internet of Things,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 2, no. 3, article 93, pp. 93:1-93:23, 2018. https://doi.org/10.1145/3264903.

S. Rhea, E. Wang, E. Wong, E. Atkins, and N. Storer., “Littletable: a time-series database and its uses,” Proceedings of the 2017 ACM International Conference on Management of Data, 2017, pp. 125–138. https://doi.org/10.1145/3035918.3056102.

I. Sokolov, I. Turkin, “Resource efficient data warehouse optimization,” Proceedings of the 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), Kyiv, 2018, pp. 491-495. https://doi.org/10.1109/DESSERT.2018.8409183.

Texas Instruments, 2.4-GHz Bluetooth low energy system-on-chip, 2013. [Online]. Available at: http://www.ti.com/lit/ds/symlink/cc2540.pdf

Texas Instruments, Cc2640 simplelink bluetooth wireless mcu, 2016. [Online]. Available at: http://www.ti.com/lit/ds/swrs176b/swrs176b.pdf

STM32L562QE, 2019. [Online]. Available at: http://www.st.com/en/microcontrollers-microprocessors/stm32l562qe.html

T. Bose, S. Bandyopadhyay, S. Kumar, A. Bhattacharyya, and A. Pal, “Signal characteristics on sensor data compression in IoT – An investigation,” Proceedings of the 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2016, pp. 1–6. https://doi.org/10.1109/SAHCN.2016.7733016.

A. Ukil, S. Bandyopadhyay, and A. Pal. “IoT data compression: Sensor-agnostic approach,” Proceedings of the IEEE Data Compression Conference (DCC), 2015, pp. 303–312. https://doi.org/10.1109/DCC.2015.66.

M. P. Andersen and D. E. Culler, “Btrdb: Optimizing storage system design for timeseries processing,” Proceedings of the FAST, 2016, pp. 39–52.

T. Pelkonen, S. Franklin, J. Teller, P. Cavallaro, Q. Huang, J. Meza, and K. Veeraraghavan, “Gorilla: A fast, scalable, in-memory time series database”, Proceedings of the VLDB Endowment, vol. 8, issue 12, pp. 1816–1827, 2015. https://doi.org/10.14778/2824032.2824078.

Information technology – generic coding of moving pictures and associated audio information – part 7: Advanced audio coding (aac), 2006. [Online]. Available at: https://www.iso.org/standard/43345.html

N. Verma, A. Shoeb, J. Bohorquez, J. Dawson, J. Guttag, and A. P. Chandrakasan, “A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system,” IEEE Journal of Solid-State Circuits, vol. 45, issue 4, pp. 804–816, 2010. https://doi.org/10.1109/JSSC.2010.2042245.

N. Q. V. Hung, H. Jeung, and K. Aberer, “An evaluation of model-based approaches to sensor data compression,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, issue 11, pp. 2434–2447, 2013. https://doi.org/10.1109/TKDE.2012.237.

C. E. Shannon, “Communication in the presence of noise,” Proceedings of the IRE, vol. 37, no. 1, pp. 10-21, 1949. https://doi.org/10.1109/JRPROC.1949.232969.

J. F. De Castro Mota, E. Zimos, M. Rodrigues, N. Deligiannis, “Internet-of-things data aggregation using compressed sensing with side information,” Proceedings of the 23rd International Conference on Telecommunications (ICT), Thessaloniki, Greece, May 16-18, 2016, pp. 402-406.

W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes: The Art of Scientific Computing, 3nd ed., Cambridge University Press, 2007, 1262 p.

K. R. Rao, P. Yip, Discrete Cosine Transform: Algorithms, Advantages, Applications, Academic Press, Boston, 1990, 490 p. https://doi.org/10.1016/B978-0-08-092534-9.50007-2.

K. Pothuganti, A. Chitneni, A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi, 2014, [Online]. Available at: https://www.iso.org/standard/43345.html

STM32L5 Series microcontroller ultra-low-power features overview, 2020, [Online]. Available at: https://www.st.com/resource/en/application_note/an5213-stm32l5-series-microcontroller-ultralowpower-features-overview-stmicroelectronics.pdf.

Downloads

Published

2022-03-30

How to Cite

Manzhos, Y. S., & Sokolova, Y. V. (2022). A Method of IoT Information Compression. International Journal of Computing, 21(1), 100-110. https://doi.org/10.47839/ijc.21.1.2523

Issue

Section

Articles