ВЕРИФИКАЦИЯ ИМИТАЦИОННОЙ МОДЕЛИ АЛГОРИТМА МАРШРУТИЗАЦИИ ADAPTIVE RATE FULL ECHO, РАЗРАБОТАННОЙ В СРЕДЕ ИМИТАЦИОННОГО МОДЕЛИРОВАНИЯ ANYLOGIC
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

VERIFICATION OF THE SIMULATION MODEL OF THE ADAPTIVE RATE FULL ECHO ROUTING ALGORITHM DEVELOPED IN THE ANYLOGIC SIMULATION ENVIRONMENT

Shilova Y.A.  

UDC 004.724.4
DOI: 10.26102/2310-6018/2019.27.4.003

  • Abstract
  • List of references
  • About authors

The mesh topology and point-to-point exchange wireless networks actualize the task of developing algorithms that increase the efficiency of routing these networks. An important feature of these networks is to use the limited battery life devices. The algorithm development taking into account battery level is an urgent task as this factor is one of the important factors affecting the network as a whole. Preview articles the author developed a new Adaptive Rate Full Echo routing algorithm, which is based on the Q-Routing algorithm, using the reinforced machine learning methods. In addition the previous author works a simulation model was presented in the Anylogic simulation system, where the developed algorithm simulation results were performed. The simulation model Verification is a necessary condition for the correctness and reliability of the data received in it. This article presents the results of checking the adequacy of the developed simulation model of the Adaptive Rate Full Echo algorithm by comparing the simulation results with the results of field tests.

1. Shilova Yu.A. Algoritm marshrutizatsii semeystva Q-routing, osnovannyy na dinamicheskom izmenenii koeffitsientov obucheniya za schet otsenki sredney zaderzhki v seti. Vestnik Permskogo nauchnogo tsentra. 2015;(2):79-93.

2. Shilova Yu.A., Kavalerov M.V. Razrabotka algoritmov marshrutizatsii semeystva QROUTING dlya mobil'nykh ADHOC setey. Avtomatizirovannye sistemy upravleniya i informatsionnye tekhnologii: materialy Vseros. nauch.-tekhn. konf., 15 maya 2015, Perm'. Perm': Izd-vo PNIPU; 2015. s. 441-446.

3. Shilova Y., Kavalerov M., Bezukladnikov I. Full Echo Q-Routing with Adaptive Learning Rates: A Reinforcement Learning Approach to Network Routing. Proceedings of the 2016 IEEE North West Russia Section Young Researchers in Electrical and Electronic Engineering Conference, 2016 ElConRusNW, 2-3 February 2016, St. Petersburg. St. Petersburg: St. Petersburg Electrotechnical Univ. LETI; 2016. p. 365-368.

4. Boyan J., Littman M. Packet Routing In Dynamically Changing Networks: A Reinforcement Learning Approach. Advances In Neural Information Processing Systems. 1994:671-678.

5. Kumar S., Miikkulainen R. Dual Reinforcement Q-Routing: An On-Line Adaptive Routing Algorithm. Artificial neural networks in engineering. 1997: 231-238.

6. Choi S., Yeung D.Y., Predictive Q-routing: A memory-based reinforcement learning approach to adaptive traffic control. Advances in Neural Information Processing Systems. 1996;(8):945-951.

7. Subramanian D., Druschel P., Chen J. Ants and reinforcement learning: A case study in routing in dynamic networks. IJCAI (2); 1997. p. 832-839.

8. Tao N., Baxter J., Weaver L. A Multi-Agent Policy-Gradient Approach to Network Routing. ICML, 2001;1. p. 553-560.

9. Sutton R.S., Barto A.G. Reinforcement learning: An introduction. ambridge: MIT press; 1998.

10. Watkins C., Dayan P. Q-learning. Machine learning.1992;8(3):279-292.

11. Shilova Yu.A., Yuzhakov A.A., Bezukladnikov I.I., Kavalerov M.V. Vliyanie koeffitsienta «skorost' rasprostraneniya ekho» na effektivnost' marshrutizatsii algoritma AdaptiveRateFullEcho. Vestnik IzhGTU. 2019;22(2):65-72

12. Shilova Yu.A., Kon E.L. Modelirovanie bazovogo protokola Q-Routing v srede Anylogic. Problemy tekhniki i tekhnologiy telekommunikatsiy (PTiTT-2016): materialy XVII mezhdunar. nauch.-tekhn. konf., 22-24 noyabrya 2016, Samara. Samara; 2016. c. 131-132.

13. Shilova Y.A. , Bezukladnikov I.I. Influence of the battery life parameter on the Q-routing algorithm results. 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus): Proc. of the 2017 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference (2017 ElConRus), 1-3 Febr. 2017, St. Petersburg, Moscow. St. Petersburg: IEEE; 2017. p. 213-217.

Shilova Yulia Aleksandrovna

Email: marissaspiritte@mail.ru

Perm National Research Polytechnic University

Perm, Russian Federation

Keywords: special network, routes, algorithm, delivery time, simulation, verification of the simulation model, network loss time connectivity

For citation: Shilova Y.A. VERIFICATION OF THE SIMULATION MODEL OF THE ADAPTIVE RATE FULL ECHO ROUTING ALGORITHM DEVELOPED IN THE ANYLOGIC SIMULATION ENVIRONMENT. Modeling, Optimization and Information Technology. 2019;7(4). Available from: https://moit.vivt.ru/wp-content/uploads/2019/11/Shilova_4_19_1.pdf DOI: 10.26102/2310-6018/2019.27.4.003 (In Russ).

622

Full text in PDF