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T-Comm_Article 2_11_2021

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REGENERATION OF DISCRETE SIGNALS MISSING SAMPLES IN THE INTERNET OF THINGS APPLICATIONS AT DIGITAL INFORMATION TRANSMITTING VIA INFOCOMMUNICATION CHANNELS

Ekaterina V. Prohorova, Surgut State University, Postgraduate Student, Techer of Dept. of Radioelectronics and Electricity, prohorova_ev@surgu.ru
Vitaliy V. Ryzhakov, Surgut State University, Head of Dept. of Radioelectronics and Electricity, ryzhakov_vv@surgu.ru

Abstract
The paper discusses a method for recovering missing samples of an analog signal during transmission over communication channels in applications of the Internet of Things. The aim of the work is to obtain a mathematical description of the procedure for restoring the values of the signal samples from the output of the analog sensor on the receiving side, which were not transmitted in order to reduce the load on data transmission channels. The procedure is based on the well-known principles of adaptive signal processing, based on the dynamic determination of the parameters of digital filters based on the assessment of the least-mean-square (LMS) deviation of the signal passing through the filter from a reference signal obtained in one way or another. A feature of the proposed method is the solution of the inverse problem of restoring the samples of the original signal with the known parameters of the filter and the reference signal. In this work, the problem of skipping and restoring samples of a discrete signal is formulated, an expression is obtained for the objective function of the method for restoring missing discrete samples, as well as an expression for iterative restoration by Newton’s method of the values of the samples of the original analog signal on the receiving side, which were not transmitted via the data transmission channel. The conditions for the applicability of the method are established, which consist in the a priori known parameters of the reference signal and the digital filter, which are determined in advance from the known characteristics of the original signal. Filtration and transmission of electrocardiogram signals through communication channels, for which an electrocardiogram can be obtained as a reference form, as the norm for healthy patients, is considered as a problem for the solution of which the proposed method is applicable. The practical significance of the proposed method lies in the organization of distributed computing for IoT systems, for which it is critically important to ensure energy savings of an autonomous power source for sensors and reduce the load on data transmission channels.

Keywords: Internet of Things, digital filtering, adaptive signal processing, discrete signals, communication channels, data transmission

References

1. S. Haykin and B. Widrow (2003), Least-Mean-Square Adaptive Filters, Hoboken, NJ: Wiley -Interscience, 502 p.
2. Widrow B. and Stearns S (1989), Adaptive Signal Processing, Radio and communication, 440 p.
3. Dzhigan V. I. (2013), Adaptive signal filtering: theory and algorithms, Technosphere, 528 p.
4. Lee P. (2019), Architecture of the Internet of Things, DMK Press, 454 p.
5. Parshin A. Yu., Parshin Yu. N. (2020), Experimental study of adaptive signal processing against the background of flicker noise, Radio engineering, Vol. 84, No. 11(21). P. 72-81.
6. Terentyev M. N. (2018), Performance indicators of discrete wireless networks of the Internet of things, Scientific and technical bulletin of the Volga region, No. 11. Pp. 258-260.
7. Kanishchev V.V., Kotenev D.D. (2018), Development of a human condition monitoring system based on data on heart rate variability, Problems of Science and Education. No. 8 (20). Pp. 36-39.
8. Md Masud Rana, Md Kaisar R. Khan and Ahmed Abdelhadi (2020), IoT Architecture for Cyber-Physical System State Estimation Using Unscented Kalman Filter, Second International Conference on Inventive Research in Computing Applications (ICIRCA), Publisher: IEEE, DOI: 10.1109/ICIRCA48905.2020.9183350
9. Manus Henry (2020), Low cost, low pass Prism filtering, 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, Publisher: IEEE, DOI: 10.1109/MetroInd4.0IoT48571.2020.9138232.
10. Md Khalid Hossain Jewel, Rabiu Sale Zakariyya and Fujiang LinA (2020), Pilot-based Hybrid and Reduced Complexity Channel Estimation Method for Downlink NB-IoT Systems, 2020 IEEE MTT-S International Wireless Symposium (IWS), Publisher: IEEE, DOI: 10.1109/IWS49314.2020.9360009.
11. Hany Hammad (2018), New Tecnhique for IoT indoor localization by employing segmented UHF RFID bandwith using bandpass filters and circulators, 2018 18th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), Publisher: IEEE, DOI: 10.1109/ANTEM.2018.8573027.
12. Abhishek Ambede, A. P. Vinod and Shanker Shreejith (2017), Efficient FPGA implementation of a variable digital filter based spectrum sensing scheme for cognitive IoT systems, 2017 Global Internet of Things Summit (GIoTS), Publisher: IEEE, DOI: 10.1109/GIOTS.2017.8016230.
13. Bart J. Thijssen, Eric A. M. Klumperink, Philip Quinlan and Bram Nauta (2019), A 0.06-3.4-MHz 92- ? W Analog FIR Channel Selection Filter With Very Sharp Transition Band for IoT Receivers, IEEE Solid-State Circuits Letters, Vol. 2, Issue 9, Sept. 2019. Pp. 171-174, DOI: 10.1109/LSSC.2019.2935569.
14. Jason Chao, Dennis Liu, Skid Chiu, Chia Shy Chang and Heinz Ru (2019), Ultra-miniature SAW filter new structure: for 5G IoT mobile device, 2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Publisher: IEEE, DOI: 10.1109/IMPACT47228.2019.9024957.
15. Md Anam Mahmud, Ahmed Abdelgawad, Kumar Yelamarthi and Yasser A Ismail (2017), Signal processing techniques for IoT-based structural health monitoring, 2017 29th International Conference on Microelectronics (ICM), Publisher: IEEE, DOI: 10.1109/ICM.2017.8268825.
16. Cheng Kang, Sizheng Chen, Na Yan, Yunyong Yu and Hao Min (2018), A Low-power Third-Order Butterworth Filter for NB-IoT Application, 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Publisher: IEEE, DOI: 10.1109/ICSICT.2018.8564966.
17. Aghus Sofwan, Sumardi and Nely Ulwiyati (2018), Filtering for Data Acquisition on Wireless Sensor Network, 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Publisher: IEEE, DOI: 10.1109/ICITACEE.2018.8576940.
18. Emmanuel Oyekanlu and Kevin Scoles (2018), Towards Low-Cost, Real-Time, Distributed Signal and Data Processing for Artificial Intelligence Applications at Edges of Large Industrial and Internet Networks, 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Publisher: IEEE, DOI: 10.1109/AIKE.2018.00037.
19. Strogonov A.V. (2015), Digital signal processing in the basis of programmable logic integrated circuits: textbook, Voronezh State Technical University, URL: https://cchgeu.ru/upload/iblock/206/osnovy-tsos-v-plis_2015.pdf (date accessed: 05/08/2021).
20. Anirut Trakultritrung, Ekkawin Thanangchusin and Sorawat Chivapreecha (2012), Distributed arithmetic LMS adaptive filter implementation without look-up table, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Publisher: IEEE, DOI: 10.1109/ECTICon.2012.6254284.
21. N. Gowtham and P. Babu (2014), An efficient architecture for BLMS adaptive filter based on distributed arithmetic technique, 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Publisher: IEEE, DOI: 10.1109/ICGCCEE.2014.6921425.
22. S. Raghunadha Reddy and P. JayaKrishnan (2017), ASIC implementation of distributed arithmetic in adaptive FIR filter, 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Publisher: IEEE, DOI: 10.1109/ICCPCT.2017.8074252.
23. Walter Huang and David V. Anderson (2009), Adaptive filters using modified sliding-block distributed arithmetic with offset binary coding, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, DOI: 10.1109/ICASSP.2009.4959641.
24. Atul A. Chandekar and Mahesh Pawar (2017), Delay and power optimized adaptive filter using distributed arithmetic, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Publisher: IEEE, DOI: 10.1109/ICECA.2017.8203682.
25. Ushenina I. (2021), Calculation, analysis and implementation of an FIR filter on FPGA using the FIR Compiler 7.2 IP module, Components and technologies, No. 2(235), P. 57-64.
26. Khludenyov A.V. (2020), Effective implementation of an FIR filter on FPGA, Actual problems and prospects in the field of engineering training. Pp. 239-245.
27. Mukhin A.E. and Ulanov P.N. (2020), Application of FPGA for Signal Processing in DDC SDR Receiver, High Performance Computing Systems and Technologies. Vol. 4. No. 1. Pp. 46-50.
28. Soloviev V.V. (2020), Design of functional blocks of embedded systems on FPGA, Hotline – Telecom, 348 p.
29. Md Anam Mahmud, Kyle Bates, Trent Wood, Ahmed Abdelgawad and Kumar Yelamarthi (2018), A complete Internet of Things (IoT) platform for Structural Health Monitoring (SHM), 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Publisher: IEEE, DOI: 10.1109/WF-IoT.2018.8355094.
30. Du Yong Kim, Ju Hong Yoon, Yong Hoon Kim and Vladimir Shin (2010), Distributed information fusion filter with intermittent observations, 2010 13th International Conference on Information Fusion, Publisher: IEEE, DOI: 10.1109/ICIF.2010.5711988.
31. Haiyang Yu, Yisha Liu and Wei Wang (2014) Distributed sparse signal estimation in sensor networks using H-consensus filtering, IEEE/CAA Journal of Automatica Sinica, Vol. 1, Issue 2, April 2014., Pp. 149-154, DOI: 10.1109/JAS.2014.7004544
32. Hui Long, Zhihua Qu, Xiaoping Fan and Shaoqiang Liu (2012), Distributed extended kalman filter based on consensus filter for wireless sensor network, Proceedings of the 10th World Congress on Intelligent Control and Automation, Publisher: IEEE, DOI: 10.1109/WCICA.2012.6359205.
33. Eremin O. Yu. And Stepanova M. V. (2020), Organization of distributed computing in the infrastructure of the Internet of things based on reinforcement learning methods, Mathematical methods in engineering and technology. Vol 12-3. Pp. 111-114.
34. Nefedova I.S., Finogeev A.A. and Finogeev A.G. (2016), Distributed data processing in wireless sensor networks based on a multi-agent approach and fog computing, Proceedings of the International Symposium «Reliability and Quality», Penza: Penza State University, Vol. 1. Pp. 258-260.
35. Lyashchenko A.M., Manucharyan L.Kh. and Pachev A.N. (2019), Implementation of IoT applications using the AKKA agent model, Engineering Bulletin of Don, No. 5(56). P. 32.
36. Al-Mardi Mohammed Haydar Awadh (2018), Features of distributed computing, taken into account in methods of optimizing algorithms for the volume of interprocessor transfers, Computer tools in education. No. 2. Pp. 31-38.
37. V. V. Ryzhakov and E. V. Prohorova (2020), Electrocardiogram Signals Digital Processing in a Distributed Computing System, 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), Publisher: IEEE, DOI: 10.1109/SYNCHROINFO49631.2020.9166080.

Information about authors:

Ekaterina V. Prohorova, Surgut State University, Postgraduate Student, Techer of Dept. of Radioelectronics and Electricity, prohorova_ev@surgu.ru
Vitaliy V. Ryzhakov, Surgut State University, Head of Dept. of Radioelectronics and Electricity, ryzhakov_vv@surgu.ru