ISSN 0021-3454 (print version)
ISSN 2500-0381 (online version)
Menu

2
Issue
vol 67 / February, 2024
Article

DOI 10.17586/0021-3454-2021-64-9-709-719

UDC 004.75

METHOD OF INTELLIGENT QUASI-INDIFFERENT DATA AGGREGATION IN HETEROGENEOUS WIRELESS SENSOR NETWORKS

A. M. Pavlov
Kursk State University, Department of Software and Information Systems Administration; Assistant


Read the full article 

Abstract. A new method for intelligent quasi-indifferent data aggregation of heterogeneous wireless sensor networks is presented. The essence of the method consists in converting the initial vectors of values recorded by the sensor nodes to vectors of values of the coefficients of piecewise polynomial regression containing a smaller number of elements, as well as in forming them into groups by calculating the Chebyshev distance with subsequent comparison with a threshold value. The developed method includes the stage of data adaptation to anomalies, which is provided by calculating and comparing with the threshold value of the scaled mean absolute deviation. The method is focused on the use in the hardware and software logic of local computing devices — sensor nodes — and can serve as a basis when designing various routing protocols for heterogeneous wireless sensor networks.
Keywords: aggregation, heterogeneous wireless sensor network, sensor node, communication channel, method of intelligent quasi-indifferent data aggregation

References:
  1. Lea P. Internet of Things for Architects, Packt Publishing, 2018, 524 p.
  2. Makhrov S.S. Ispol'zovaniye neyronnykh mekhanizmov iskusstvennogo intellekta dlya klasterizatsii uzlov i marshrutizatsii dannykh v besprovodnykh sensornykh setyakh (Using Neural Mechanisms of Artificial Intelligence for Clustering Nodes and Routing Data in Wireless Sensor Networks), Candidate’s thesis, Moscow, 2015, 145 р. (in Russ.)
  3. Mutkhanna A.S.A. Issledovaniye trafika i protokolov marshrutizatsii v besprovodnykh setyakh Investigation of traffic and routing protocols in wireless networks Candidate’s thesis, St. Petersburg, 2016. 176 р. (in Russ.)
  4. Bershadsky A.M., Kurilov L.S., Finogeev A.G. University Proceedings. Volga Region. Technical Sciences, 2012, no. 1(21), pp. 47–57. (in Russ.)
  5. Tarakanov E.V. Agregirovaniye dannykh mul'tisensorov v besprovodnykh sensornykh setyakh (Aggregation of Multisensor Data in Wireless Sensor Networks), Candidate’s thesis, Tomsk, 2012, 95 р. (in Russ.)
  6. Golubnichaya E.Yu. Problemy peredachi informatsii v infokommunikatsionnykh sistemakh (Problems of Information Transmission in Infocommunication Systems), Collection of reports and abstracts of the XIII All-Russian Scientific and Practical Conference (May 26, 2017), Volgograd, 2017, рр. 37–42. (in Russ.)
  7. Kolchin M.A., Shilin I.A., Klimov N.V., Garaizuev D.S., Muromtsev D.I., Zakoldaev D.A. Journal of Instrument Engineering, 2015, no. 11(58), pp. 945–951, DOI: 10.17586/0021-3454-2015-58-11-945-951. (in Russ.)
  8. Kremer N.Sh. Teoriya veroyatnostey i matematicheskaya statistika (Probability Theory and Mathematical Statistics), Moscow, 2010, 551 р. (in Russ.)
  9. Krasnov M.L., Kiselev A.I., Makarenko G.I., Shikin E.V., Zalyapin V.I., Sobolev S.K. Vsya vysshaya matematika: Uchebnik. Tom 5 (All Higher Mathematics: Textbook. Vol. 5), Moscow, 2001, 296 р. (in Russ.)
  10. Sklar B. Digital communications, Prentice Hall, January 21, 2001, 1079 p.
  11. Omel'chenko A.V. Teoriya grafov (Graph Theory), Moscow, 2018, 416 р. (in Russ.)
  12. D'yakonov V.P. MATLAB. Polnyy samouchitel' (MATLAB. Complete tutorial), Moscow, 2012, 768 р. (in Russ.)
  13. https://td.chem.msu.ru/study/generalcourses/statdatatreatment/. (in Russ.)
  14. Nasteka A.V., Kanev A.N., Bessonova Е.Е. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, no. 3(17), pp. 450–456. (in Russ.)
  15. Ivanov O.V. Statistika/Uchebnyy kurs dlya sotsiologov i menedzherov. Chast' 1. Opisatel'naya statistika. Teoretiko-veroyatnostnyye osnovaniya statisticheskogo vyvoda (Statistics/Training Course for Sociologists and Managers. Part 1. Descriptive Statistics. Probabilistic Foundations of Statistical Inference), Moscow, 2005, 187 р. (in Russ.)
  16. Erokhin S.D., Makhrov S.S. T-Comm, 2013, no. 3, pp. 44–47. (in Russ.)