THE METHOD OF DATA COMPRESSION IN INTERNET OF THINGS COMMUNICATION

Юрій Семенович Манжос, Євгенія Віталіївна Соколова

Abstract


The Internet of Things (IoT) is a modern paradigm consisting of heterogeneous intercommunicated devices that sending and receiving messages in various formats through different protocols. Thanks to the everywhere use of smart things, 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, using Bluetooth Low Energy costs tens of millijoules per connection, while computing at full energy costs only tens of microjoules, and sitting idle costs close to one microjoule per second for STM processors. That is why additional data compression for smart devices can decrease the energy costs of IoT. There are methods of data compression without or with information loss. It is mathematically proved, that it is possible to construct as arbitrarily close approximations of a weighted sum of generalized orthogonal polynomials to an input function (IoT data). In this article, we are researching the Chebyshev and Fourier sequences as an approximation of source data. For a different type of data in the different sequences, we have a different compression for Chebyshev and Fourier approximation. Concurrent use of transformations allows selecting a maximal compression for different sequences. This article proposes a compression method especially suited for IoT devices. The proposed method is based on the simultaneous use of Chebyshev and Fourier transforms. To improve the compression performance was used a trigonometric optimization. The modification of Chebyshev transformation allows reducing energy costs by about four times. Trigonometric optimization replaces the direct use of the mathematical function cos(x) in a double loop by iteration expressions. A modified algorithm uses a one-time calculation of the cos(x) function. As a result, we have a slight increase of the source code and decrease of the computation time, and increasing energy effectiveness. The software implementation in C ++ of the modified Chebyshev transformation algorithm was proposed. The proposed method can be used not only in IoT but also for the accumulation of data on big servers.

Keywords


Internet of Things; lossy signal compression; data approximation; general orthogonal polynomials; Fourier transformation; modification of Chebyshev discrete transformation; trigonometric optimization; energy effectiveness

References


Bai, L. S., Dick, R. P., Dinda, P. A. Archetype-based design: Sensor network programming for application experts, not just programming experts. International Conference on Information Processing in Sensor Networks, Washington, IEEE Computer Society Publ., 2009, pp. 85–96.

IoT: number of connected devices worldwide 2012–2025 Available at: https://camrojud.com/%E2%80%A2-iot-number-of-connected-devices-worldwide-2012-2025/ (accessed 10.11.2019).

Miorandi, D., Sicari, S., Pellegrini, F., Chlamtac I. Internet of Things: Vision, applications and research challenges. Ad Hoc Networks, 2012, no. 10 (7), pp. 1497–1516.

A Survey on Sensor-based Threats to Internet-of-Things (IoT) Devices and Applications Available at: https://arxiv.org/pdf/1802.02041.pdf (accessed 01.06.2019).

Вasics of the I2C communication protocol Available at: https://www.circuitbasics.com/basics-of-the-i2c-communication-protocol (accessed 12.02.2016).

Serial Peripheral Interface Available at: https://en.wikipedia.org/wiki/Serial_Peripheral_Interface (accessed 29.08.2019).

Wi-Fi certified 6 Available at: https://www.wi-fi.org/discover-wi-fi/wi-fi-certified-6 (accessed 3.06.2020).

Bluetooth Core Specification Version 5.0 Feature Overview Available at: https://www.bluetooth.com/bluetooth-resources/bluetooth-5-go-faster-go-further/ (accessed 30.04.2018).

Adelantado F., Vilajosana X., Tuset-Peiro P., Martinez B., Melia J., Watteyne T. Understanding the limits of LoRaWAN. IEEE Communications Magazine Available at: https://arxiv.org/abs/1607.08011 (accessed 13.02.2017).

IoT Explained How Does an IoT System Actually Work? Available at: https://medium.com/iotforall/iot-explained-how-does-an-iot-systemactually-work-e90e2c435fe7 (accessed 01.06.2019).

ISO/IEC 20922:2016 Information technology -- MQ Telemetry Transport (MQTT) v3.1.1 Available at: https://www.iso.org/standard/69466.html (accessed 08.06. 2016).

MQTT: The Standard for IoT Messaging Available at: https://mqtt.org. (accessed 12.01.2015).

Obermaier, D. MQTT Monday Is Back Introducing the MQTT Essentials Video Series Available at: https://www.hivemq.com/blog/mqtt-essentials-video-series/ (accessed 03.08.2020).

MQTT Version 5.0 OASIS Standard Specification URIs. Available at: https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html (accesed 07.03.2019).

Buevich, M. Respawn: a distributed multi-resolution time-series datastore. IEEE 34th Real-Time Systems Symposium, 2013, pp. 288–297.

Blalock, D., Madden, S., Guttag, J. Sprintz: Time Series Compression for the Internet of Things. Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 3, Article 93, ACM Publ., 2018, pp. 1 23.

Rhea, S., Wang, E., Atkins, E., Storer N. Littletable: a time-series database and its uses. Proceedings of the 2017 ACM International Conference on Management of Data, ACM Publ., 2017, pp. 125–138.

Sokolov, I., Turkin, I. Resource efficient data warehouse optimization. Dependable Systems, Services and Technologies Proceedings of the 9th International IEEE Conference, Kyiv, IEEE Publ. 2018, pp. 491-495.

T. Instruments. 2.4-GHz Bluetooth low energy system-on-chip. Available at: http://www.ti.com/lit/ds/symlink/cc2540.pdf (accessed 01.06.2013).

T. Instruments. CC2640 simplelink bluetooth wireless MCU. Available at: https://www.ti.com/lit/ds/symlink/cc2640.pdf?ts=1603975816936&ref_url=https%253A%252F%252Fwww.ti.com%252Fproduct%252FCC2640 (accessed 09.12.2020).

STM32L562хх. Available at: https://www.st.com/resource/en/datasheet/stm32l562qe.pdf (accessed 01.03.2020).

Bose, T., Bandyopadhyay, S., Kumar, S., Bhattacharyya, A., Pal A. Signal characteristics on sensor data compression in IoT- An investigation. 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), IEEE Publ. 2016, pp. 1-6.

Ukil, A., Bandyopadhyay, S., Pal, A. Iot data compression: Sensor-agnostic approach. In Data Compression Conference (DCC), IEEE Publ. 2015, pp. 303–312.

Andersen, M. P., Culler, D. E. BTrDB: Optimizing storage system design for timeseries processing, 14th USENIX Conference on File and Storage Technologies (FAST’16), USENIX Association Publ. 2016, pp. 39–52.

Pelkonen, T., Franklin, S., Teller, J., Cavallaro, P., Huang, Q., Meza, J., Veeraraghavan, K. Gorilla: A fast, scalable, in-memory time series database. Proceedings of the VLDB Endowment, Vol. 8, № 12, VLDB Endowment Publ. 2015, pp.1816–1827.

ISO/IEC 13818-7:2006. Information technology – generic coding of moving pictures and associated audio information. Available at: https://www.iso.org/standard/43345.html (accessed 15.01.2006).

Verma, N., Shoeb, A., Bohorquez, J., Dawson, J., Guttag, J., Chandrakasan A. P. 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, iss. 4, IEEE Publ. 2010, pp. 804–816.

Hung, N. Q. V., Jeung, H., Aberer, K. An evaluation of model-based approaches to sensor data compression. IEEE Transactions on Knowledge and Data Engineering, vol. 25, iss. 11, IEEE Publ. 2013, pp. 2334–2447.

Shannon, C. E. Communication in the presence of noise. Proceedings of the IRE, vol. 37, iss. 1, Publ. 1949, pp. 10-21.

Huffman, D. A Method for the Construction of Minimum-Redundancy Codes. Proceedings of the IRE, vol. 40, iss. 9, Publ. 1952, pp. 1098-1101.

Zimos, E., Mota, J. F., Rodrigues, M., Deligiannis, N. Internet-of-things data aggregation using compressed sensing with side information. Proceedings of the 23rd International Conference on Telecommunications (ICT), IEEE Publ. 2016, pp. 1-5.

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




DOI: https://doi.org/10.32620/reks.2020.4.05

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