1. MMC
  2. All Volumes and Issues
  3. Volume 7, Number 2, 2020
  4. A system of indicators and criteria for evaluation of the level of functional stability of information heterogenic networks

A system of indicators and criteria for evaluation of the level of functional stability of information heterogenic networks

MMC.
2020;
Volume 7, Number 2
: pp. 285–292
https://doi.org/10.23939/mmc2020.02.285
Received: November 01, 2019
Revised: July 11, 2020
Accepted: July 15, 2020

Mathematical Modeling and Computing, Vol. 7, No. 2, pp. 285–292 (2020)

Authors:
  1. O. V. Maksymuk
  2. V. V. Sobchuk
  3. I. P. Salanda
  4. Yu. V. Sachuk
1
Ivan Franko National University of Lviv
2
Lesya Ukrainka East European National University
3
Taras Shevchenko Regional Humanitarian and Pedagogical Academy of Kremenets
4
Lesya Ukrainka Eastern European National University

The mechanisms of self-organization of information heterogeneous networks have been analyzed in this article and new indicators and criteria for defining functionally stable networks have been suggested in accordance with the concept of SON, as well as a mathematical model of relevant network processes based on hypergraphs providing the required parameters and performance indicators of the mentioned hypernet has been rigorously substantiated.  Due to the suggested indicators and criteria, we can evaluate and compare different structures of the high-connectivity networks, and apply them to the creation of a methodology for optimal use of the system redundancy when parrying the effects of accidental situations.  It is expedient to use these indicators for up-to-date and advanced networks that are wireless, dynamic, self-organizing since, under the restructuring, they allow taking into account a certain number of elements, the destruction of which does not affect the functional stability of the network.

functional stability
information heterogeneous network
self-organization
hypernet
  1. Honda H.  On a model of target detection in molecular communication networks.  Networks & Heterogeneous Media. 14 (4), 633–657 (2019).
  2. Nicola W., Campbell S. A.  Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons.  Front. Comput. Neurosci. 7, Article 184, 1–25 (2013).
  3. Honda H.  Local-in-time solvability of target detection model in molecular communication network.  International Journal of Applied Mathematics. 31 (3), 427–455 (2018).
  4. Yaguache F. R., Ahola K.  Containerized services orchestration for edge computing in software-defined wide area networks.  International Journal of Computer Networks and Communications.  11 (5), 113–132 (2019).
  5. Kozelkov S., Bondarchuk A., Storchak K., Polovenya S., Miller Yu.  Matematicheskaya model raspredeleniya informatsionnyih potokov.  Problemy infokommunikatsiy. 1 (7), 13–22  (2018), (in Russian).
  6. Memos V. A.  Efficient Multimedia Transmission over Scalable IoT Architecture.  International Journal of Computer Network and Information Security. 10 (6), 27–39 (2018).
  7. Kulin M., Fortuna C., De Poorter E., Deschrijver D., Moerman I.  Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial.  Sensors. 16 (6), 790–796 (2016).
  8. Salanda I. Barabash O., Musienko A.  Systema pokaznykiv ta kryteriiv formalizatsii protsesiv zabezpechennia lokalnoi funktsionalnoi stiikosti rozghaluzhenykh informatsiinykh merezh. Naukove periodychne vydannia "Systemy upravlinnia, navihatsii ta zviazku". 1 (41), 122–126 (2017), (in Ukrainian).
  9. Popkov V. K.  Matematicheskie modeli zhivuchesti setey svyazi. Novosibirsk, Computing Center of SD AS USSR  (1990), (in Russian).