Abstract
In this paper presented method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can use cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach focuses on sensor processing. Correlation and autocorrelation methods for event detection depending on waveform are described in detail and implementation of endpoint architecture is proposed. Novelty and another feature of this approach is the use of not the full waveform, but their components and processing on the device. This significantly reduces the number of operations and complexity of implementation. Other methods focus on the cloud computing paradigm. The results of the simulation show that at the data transfer rate from the sensor ~10 MSample/s, the proposed method allows you to transmit and store 280 bytes in 70 min instead of 157 GB using the bypass method. Reducing data transfer and storage requirements will simplify and reduce the cost of IoT systems, improve performance, and apply additional precision sensors to provide more accurate data.
The solutions are focused on low-power and FPGA/ASIC implementations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Khan, R., Khan, S.U., Zaheer, R., Khan, S.: Future internet: the internet of things architecture, possible applications and key challenges. In: Proceedings of the 10th International Conference on Frontiers of Information Technology (FIT 2012), pp. 257–260, December 2012
Weyrich, M., Ebert, C.: Reference architectures for the internet of things. IEEE Softw. 33(1), 112–116 (2016)
Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169–186. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05029-4_7
Marjani, M., et al.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017)
Engines in the Data Cloud. https://www.digitalcreed.in/engines-data-cloud/. Accessed 10 Apr 2018
Bhuiyan, M.Z.A., Wu, J., Wang, G., Wang, T., Hassan, M.M.: E-sampling: event-sensitive autonomous adaptive sensing and low-cost monitoring in networked sensing systems. ACM Trans. Auton. Adapt. Syst. 12 (2017)
Harb, H., Makhoul, A.: Energy-efficient sensor data collection approach for industrial process monitoring. IEEE Trans. Ind. Informat. 14(2), 661–672 (2018)
Tayeh, G.B., Makhoul, A., Laiymani, D., Demerjian, J.: A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks. Perv. Mob. Comput. 49, 62–75 (2018)
Tayeh, G.B., Makhoul, A., Demerjian, J., Laiymani, D.: A new autonomous data transmission reduction method for wireless sensors networks. In: Proceedings of IEEE Middle East North African Communication Conference (MENACOMM), pp. 1–6, April 2018
Braten, A.E., Kraemer, F.A., Palma, D.: Adaptive, correlation-based training data selection for IoT device management. In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Granada, Spain, pp. 169–176 (2019)
Tayeh, G.B., Makhoul, A., Perera, C., Demerjian, J.: A spatial-temporal correlation approach for data reduction in cluster-based sensor networks. IEEE Access 7, 50669–50680 (2019)
Su, S., Sun, Y., Gao, X., Qiu, J., Tian, Z.: A correlation-change based feature selection method for IoT equipment anomaly detection. Appl. Sci. 9(3), 437 (2019)
Kim, S., Lee, H., Ko, H., Jeong, S., Byun, H., Oh, K.: Pattern matching trading system based on the dynamic TimeWarping algorithm. Sustainability 10, 4641 (2018)
Ifeachor, E., Jervis, B.: Digital Signal Processing: A Practical Approach, 2nd edn., pp. 184–245. Prentice Hall, Upper Saddle River (2001)
Oppenheim, A.V., Schafer, R.W., Buck, J.R.: Discrete-Time Signal Processing, 2nd edn., pp. 746–753. Prentice Hall, Upper Saddle River (1998)
Kurose, J.F., Ross, K.W.: Computer networking : a top-down approach, 7th edn. Pearson Education Limited, London (2017). 6th edn., pp. 264–266
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Anufrienko, A. (2020). Methods for Reducing the Amount of Data Transmitted and Stored in IoT Systems. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2020 2020. Lecture Notes in Computer Science(), vol 12525. Springer, Cham. https://doi.org/10.1007/978-3-030-65726-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-65726-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65725-3
Online ISBN: 978-3-030-65726-0
eBook Packages: Computer ScienceComputer Science (R0)