S.V. Kozlov1, E.A. Spirina2

1,2 Kazan National Research Technical University named after A.N. Tupolev – KAI (Kazan, Russia)

1 SVkozlov@kai.ru, 2 EASpirina@kai.ru

To increase the heterogeneous networks capacity by effectively reducing the intra-system interference influence, it is topicaly to use the integrated optimization method of these networks. However, its application is impossible without implementing the collective dynamic routing method, which is its basis. The simplest way to implement the collective dynamic routing method is to use the software-defined networks technology, which allows solving this problem by developing only software for the controller. Therefore, the development of algorithms for implementing the collective dynamic routing method in software-defined heterogeneous networks on is an urgent task.

As the work result, algorithms for forming the initial and final links, building end-to-end one-dimensional routes, building a valid routes set, calculating the volume of data being delivered along routes that are performed only during initial network planning and changing its configuration, as well as a data transfer algorithm performed in real time, allowing the implementation of a collective dynamic routing method in software-defined heterogeneous networks were developed. All developed algorithms are implemented as an application for the RYU controller. The developed application operability was tested in the Mininet Wi-Fi network emulator with an external RYU controller using a Wi-Fi subnet as an example.

The developed algorithms and the application implementing them make it possible to apply the collective dynamic routing method to increase the software-defined heterogeneous networks throughput.

Kozlov S.V., Spirina E.A. Algorithms for implementing the collective dynamic routing method in software-defined heterogeneous networks. Electromagnetic waves and electronic systems. 2025. V. 30. № 5. P. 61−78. DOI: https://doi.org/10.18127/j15604128-202505-05 (in Russian)

1. Spirina E.A., Kozlov S.V., Bukharina A.A. Traffic model in heterogeneous networks based on experimental data. Radiotekhnika. 2024. V. 88. № 1. P. 92−110. DOI 10.18127/j00338486-202401-09 (In Russian)
2. Kozlov S., Spirina E. Novel Modification of Integrated Optimization Method for Sensor’s Communication in Wi-Fi Public Networks. Sensors. 2024. V. 24. № 5. P. 1395. DOI 10.3390/s24051395.
3. Spirina E.A., Kozlov S.V., Ismagilov E.A. Modification of the collective dynamic routing method in heterogeneous networks. Electromagnetic waves and electronic systems. 2024. V. 29. № 4. P. 96−107. DOI 10.18127/j15604128-202404-08. (in Russian)
4. Patent for invention RUS2784656 dated 29.11.2022. A method of joint dynamic routing in a communication network with packet message transmission. Kozlov S.V., Spirina E.A. (in Russian)
5. Vintenkova Y.S., Kozlov S.V., Spirina E.A. Bran collective dynamic routing optimal routes evaluation algorithm. Systems of Signals Generating and Processing in the Field of on Board Communications. Moscow, Russia. 2018. P. 1–3. DOI 10.1109/SOSG.2018. 8350578.
6. Spirina E.A., Petrova E.A. A Robust Method for Estimating the Channel Data Rate in Seamless IEEE 802.11ax Standard Networks. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). Kaliningrad, Russia. 2021. P. 1–5. DOI 10.1109/SYNCHROINFO51390.2021.9488341.
7. Bukharina A.A. Advantages of Method of Collective Dynamic Routing in IP Networks. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). Kaliningrad, Russia. 2021. P. 1–4. DOI 10.1109/SYNCHROINFO51390.2021. 9488376.
8. Stiti O., Braham O., Pujolle G. Virtual openflow-based SDN Wi-Fi access point. Global Information Infrastructure and Networking Symposium (GIIS). Guadalajara, Mexico. 2015. P. 1–3. DOI 10.1109/GIIS.2015.7347190.
9. Seyedebrahimi M., Bouhafs F., Raschellà A., Mackay M., Shi Q. SDN-based channel assignment algorithm for interference management in dense Wi-Fi networks. European Conference on Networks and Communications (EuCNC). Athens, Greece. 2016. P. 128–132. DOI 10.1109/EuCNC.2016.7561018.
10. P1930.1/D1. IEEE Draft Recommended Practice for Software Defined Networking (SDN) based Middleware for Control and Management of Wireless Networks. 2022. 136 p.
11. Costa-Requena J. SDN integration in LTE mobile backhaul networks. The International Conference on Information Networking. Phuket, Thailand. 2014. P. 264–269. DOI 10.1109/ICOIN.2014.6799479.
12. Rajalakshmi S., Deepika N., Srivardhini C.S., Vignesh A.C., Vignesh D.V. SDN Controller for LTE Networks. International Journal of Computer Applications. 2016. V. 133. № 3. P. 31–36. DOI 10.5120/ijca2016907782.
13. Park J., Yoon W. An architecture of multi-layered SDN based LTE/WiFi Network for multi-interface D2D users. International Conference on Information and Communication Technology Convergence (ICTC). Jeju, Korea (South). 2018. P. 1161–1163. DOI 10.1109/ICTC. 2018.8539595.
14. Ma L., Wen X., Wang L., Lu Z., Knopp R. An SDN/NFV based framework for management and deployment of service based 5G core network. China Communications. 2018. V. 15. № 10. P. 86–98. DOI 10.1109/CC.2018.8485472.
15. Irshad M.N., Du L., Khoso I.A., Javed T.B., Aslam M.M. A Hybrid Solution of SDN Architecture for 5G Mobile Communication to Improve Data Rate Transmission. 28th Wireless and Optical Communications Conference (WOCC). Beijing, China. 2019. P. 1–5. DOI 10.1109/ WOCC.2019.8770664.
16. Ashaev I.P., Gaysin A.K., Korobkov A.A., Safiullin I.A., Nadeev A.F. Determining User Mobility Pattern in Heterogeneous Wireless Networks with Multidimensional Data Processing. Vestnik of Volga State University of Technology. Ser.: Radio Engineering and Infocommunication Systems. 2023. № 4(60). P. 6–23. DOI 10.25686/2306-2819.2023.4.6. (in Russian)
17. Mininet-WiFi Emulation Platform for Software-Defined Wireless Networks. [Electronic resource] – Access mode: https://mininet-wifi.github.io/get-started/, date of reference 15.04.2025.
18. Santos I., Vieira P., Borralho R., Queluz M.P., Rodrigues A. Emulating a Software Defined LTE Radio Access Network Towards 5G. International Conference on Communications (COMM). Bucharest, Romania. 2018. p. 1-376. DOI 10.1109/ICComm.2018.8484764.
19. Vintenkova Yu.S., Kozlov S.V. Computational Complexity Reduction of Valid Multidimensional Routes Construction Procedure in Collective Dynamic Routing. Vestnik of Volga State University of Technology. Ser.: Radio Engineering and Infocommunication Systems. 2019. № 2(42). P. 22–30. DOI 10.25686/2306-2819.2019.2.22. (in Russian)
20. Shevelev Yu.P. Discrete mathematics. Part 2: Theory of finite automata. Combinatorics. Graph theory. Tomsk: Tomsk State University of Control Systems and Radio Electronics. 2003. 130 p. (in Russian)
21. Kormen T., Leiserson Ch., Rivest R. Algorithms: construction and analysis. Moscow: ICNMO. 2000. 960 p. (in Russian)
22. Kozlov S.V., Spirina E.A., Ashaev I., Bukharina A., Gaisin A.K. Novel Modification of the Collective Dynamic Routing Method for Sensors' Communication in Wi-Fi Public Networks. Sensors. 2022. V. 22. № 22. P. 8602. DOI 10.3390/s22228602.
23. Vintenkova Yu.S. Optimization of information distribution in broadband radio access networks in conditions of limited computing resources: diss. … Candidate of Technical Sciences. Kazan: Kazan National Research Technical University named after A.N. Tupolev – KAI. 2019. 128 p. (in Russian)
24. Bron C., Kerbosch J. Algorithm 457: finding all cliques of an undirected graph. Communications of the ACM. 1973. V. 16. № 9. P. 575–577.
25. Kozlov S.V. Data Rate Estimation Method for Wi-Fi Networks Operating under Intra-system Interference Influence. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). Svetlogorsk, Russia. 2020. P. 1–5. DOI 10.1109/SYNCHROINFO49631.2020.9166057.