Simulation model for adaptive sensor networks studies

Systems, networks and telecommunication devices


Borodin V. V.*, Petrakov A. M.**, Shevtsov V. A.***

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia



The article presents a simulation model for the adaptive sensor communication networks effectiveness study and analysis. It presents the model structure, and describes its functional capabilities and limitations. With the aim of reducing the simulation time, a number of controlling mechanisms was implemented in the model, particularly a threshold value control of the selected set of parameters, forecasting the parameters changes, with account for previously obtained results. The proposed model is used in the following studies:

– Determining the conditions of the network staying in a steady state, and its the transition to the lock state. It was shown, in particular, that the network state estimation can be performed based the analysis of the local parameters behavior (i.e. computed at each node);

– Development and optimization of multi-station access algorithms, determining the area of effective application for adaptive network management;

– Bandwidth optimization service channel to manage the network functioning process, preventing its transition to the unstable state and the lock state;

– Analysis of the input message flow view impact on the network efficiency; size optimization of the packets allocated from messages;

– Development of algorithms for optimal network management, both local and global (network-wide) parameters;

– Development and optimization of algorithms for the service channel operation for the route information transmission;

– Development and research of routing algorithms preventing duplicate packets and looping routes, as well as determining the delivery time of routing information, network load, and the amount of transmitted information;

– Studying the network behavior in the space of complex geometry (in particular, on the surface of the hemisphere, torus, etc.), as well as in the presence of opaque partitions spatially separating the network nodes;

– Studying of the network behavior and routing algorithms in non-stationary conditions, including nodes or communication channels failures, the time variation of the selected nodes activity, changing the space configuration of the, in which the network operates.

At present, according to the results of the study, a voluminouse statistical material was obtained. A part of the results of the study are reflected in references [17–20].


adaptive data transmission radio network, sensor radio networks, simulation, access methods, routing


  1. Kim N.V., Krylov I.G. IX Vserossiiskaya nauchno-tekhnicheskaya konferentsiya “Problemy sovershenstvovaniya robototekhnicheskikh i intellektual’nykh sistem letatel’nykh apparatov”. Sbornik dokladov (Moscow, 25 — 27 May 2005), Moscow, Izd-vo MAI, 2012, pp. 59 – 62.

  2. Razgulyaev L. Zarubezhnoe voennoe obozrenie, 2008, no. 1, pp. 35 – 39.

  3. Molchanov D.A. Elektrosvyaz’, 2006, no. 6, pp. 24 – 28.

  4. Nazarenko A.P., Sar’yan V.K., Lutokhin A.S., Sushchenko N.A. Elektrosvyaz’, 2015, no. 7, pp. 12 – 15.

  5. Jacquet P., Clausen T. Optimized Link State Routing Protocol (OLSR), Internet Engineering Task Force, October 2003, available at:

  6. Ogier R., Templin F., Lewis M. Topology Dissemination Based on Reverse-Path Forwarding (TBRPF), Internet Engineering Task Force, February 2004, available at:

  7. Perkins C., Belding-Royer E., Das S. Ad hoc On-Demand Distance Vector (AODV) Routing // Internet Engineering Task Force, July 2003, available at:

  8. Network Simulator 2 (NS-2), available at:

  9. Rukovodstvo po srede modelirovaniya GPSS World, available at:

  10. Boev V.D. Modelirovanie sistem. Instrumental’nye sredstva GPSS WORLD (Systems modeling. GPSS WORLD tools. Studies grant), Saint-Petersburg, BHV-St. Petersburg, 2004, 368 p.

  11. The VINT Project A Collaboration between researchers at UC Berkeley, LBL, USC/ISI, and Xerox PARC. Kevin Fall, Kannan Varadhan, November 4, 2011, URL:

  12. Development of laboratory exercises based on OPNET Modeler, OPNET, 2012. URL:

  13. Akimov E.V., Kuznetsov M.N. Trudy MAI, 2010, no. 40, available at:

  14. Attarzadeh N., Mehrani M.A New Three Dimensional Clustering Method for Wireless Sensor Networks, Global Journal of Computer Science and Technology, April 2011, vol. 11, issue 6, version 1.0.

  15. Terent’ev M.N. Vestnik Moskovskogo aviatsionnogo instituta, 2010, vol. 17, no. 3, pp. 178 – 183.

  16. Nastasin K.S. Rodionov V.V. Trudy MAI, 2011, no. 49, available at:

  17. Borodin V.V., Petrakov A.M., Shevtsov V.A. Trudy MAI, 2015, no. 81, available:

  18. Borodin V.V., Petrakov A.M. Trudy MAI, 2015, no. 80, available at:

  19. Borodin V.V. Shevtsov V.A. Trudy MAI, 2012, no. 80, available at:

  20. Borodin V.V., Petrakov A.M., Shevtsov V.A. Elektrosvyaz’, 2016, no. 11, pp. 41 – 45.

Download — informational site MAI

Copyright © 2000-2020 by MAI