DOI: https://doi.org/10.15802/stp2020/204005

ORGANIZING WIRELESS NETWORK AT MARSHALLING YARDS USING THE BEE METHOD

V. M. Pakhomova, D. I. Nazarova

Abstract


Purpose. In general, today wireless networks are widely used as an alternative to wired, allowing you to connect multiple devices, both among themselves in the local and global Internet. However, at the present stage in Ukraine there is no widespread use of a wireless network at rail transport, therefore it is advisable to conduct research on the deployment of such a network, in particular, at a marshalling yard. Methodology. Using LocBS‑BeeCol program model written in Python according to the bee colony algorithm the optimal number of base stations (BS) of the wireless network and their location at the marshalling yards was determined, as well as research on the bee algorithm parameters was conducted. Input data of the LocBS‑BeeCol model are as follows: marshalling yard parameters (area, number of clients that need to be connected to base stations); wireless network parameters (base station coverage radius, maximum number of clients for one base station); parameters of the bee colony algorithm (number of scout bees, number of attempts to find the optimal solution using one bee). Findings. For marshalling yards of various capacities (small, medium and high), the optimal number of base stations of the wireless network was obtained with restrictions on the coverage radius of the base station and the number of clients connected to it. Thus, for example, to connect 300 clients at medium-sized marshalling yards with an area of 2500x500 m2, 93 base stations with a coverage radius of 50 m are needed. Originality. The quality of the obtained solutions significantly depends on the choice of the bee colony algorithm parameters. A study of the base stations number of the wireless network and search time for finding the optimal solution for different number of bees and the number of attempts to find the optimal solution using the bee for marshalling yards of various capacities was carried out. It was determined that an increase in the number of bees (from 10 to 50) and the number of attempts to find the optimal solution by a bee (from 10 to 50) improves the quality of the optimal solution (decrease in the number of base stations by an average of 6.5% and 9.3%), respectively. In addition, increase in the bee number (from 10 to 50) reduces the search time for the optimal solution by bees by an average of 1.8 times, while increase in the number of attempts to find the optimal solution by a bee (from 10 to 50) will increase search time for the optimal solution on average 2.14 times. Practical value. An algorithm and its software implementation have been developed, which make it possible to determine the required number of base stations and their location when deploying a wireless network at a marshalling yards. For marshalling yards with high capacity, when the coverage radius of the base station is doubled (from 50 to100 m), their number decreases by about half (from 136 to 64), while the time for finding the optimal solution by bees increases by 2.5 times (from 8.4 to 20.6 s).


Keywords


marshalling yard; wireless network; base station (BS); coverage radius; bee method; bees; attempts; search time

References


Skakov, E., & Malysh, V. (2016). Bee optimization algorithm for solving wireless network planning problem. Software products and systems., 4(67), 67-73. DOI: https://doi.org/10.15827/0236-235X.115.067-073 (in Russian)

Smirnova, O., Bogoradnikova, A. & Blinov, M. (2015). Description of swarm algorithms inspired by inanimate nature and bacteria for use in the ontological model. International Journal of Open Information Technologies, 3(12), 28-37. (in Russian)

Ahamed, A., Islam, N., Soikot, M. A. S., Hossen, M. S., Ahmed, R., & Hasan, M. A. (2019). Train Collision Avoidance Using GPS and GSM Module. 2019 International Conference on Power Electronics, Control and Automation (ICPECA), 1-4. DOI: https://doi.org/10.1109/icpeca47973.2019.8975543 (in English)

Ai, B., Guan, K., Rupp, M., Kurner, T., Cheng, X., Yin, X.-F., …& Ding, J.-W. (2015). Future railway services-oriented mobile communications network. IEEE Communications Magazine, 53(10), 78-85. DOI: https://doi.org/10.1109/MCOM.2015.7295467 (in English)

A Brief Overview of the Wireless World. Retrieved from https://www.sciencedirect.com/topics/computer-science/basic-service-set

A Necessary GSM-R Mobile Upgrade. Retrieved from https://www.railengineer.co.uk/2019/03/13/a-necessary-gsm-r-mobile-upgrade

Banerjee, S., Hempel, M., & Sharif, H. (2016). A Survey of Wireless Communication Technologies & Their Performance for High Speed Railways. Journal of Transportation Technologies, 06(01), 15-29 DOI: https://doi.org/10.4236/jtts.2016.61003 (in English)

Davidovic, T., Teodorovic, D. & Selmic, M. (2015). Bee Colony Optimization – Part I: The Algorithm Overview: Invited survey. Yugoslav Journal of Operations Research, 25(1), 33-56. DOI: https://doi.org/10.2298/YJOR131011017D (in English)

Hussein, W. A., Sahran, S., & Sheikh Abdullah, S. N. H. (2017). The variants of the Bees Algorithm (BA): a survey. Artificial Intelligence Review, 47(1), 67-121 DOI: https://doi.org/10.1007/s10462-016-9476-8 (in English)

Kumar, K., Zindani, D., & Davim, J. P. (2019). Bees Algorithm. Optimizing Engineering Problems through Heuristic Techniques. DOI: https://doi.org/10.1201/9781351049580-5 (in English)

Nurmi, J., Lohan, E.-S., Wymeersch, H., Seco-Granados, G., & Nykänen, O. (2017). Multi-Technology Positioning. Springer International Publishing AG. DOI: https://doi.org/10.1007/978-3-319-50427-8 (in English)

Osterhage, W. (2018). Wireless Network Security. Taylor & Francis Group, 14-77. DOI: https://doi.org/10.1201/9781315106373-3 (in English)

Pakhomova, V. M., Skaballanovich, T. I., & Bondareva, V. S. (2019). Intelligent routing in the network of information and telecommunication system of railway transport. Science and Transport Progress, 2(80), 77-90. DOI: https://doi.org/10.15802/stp2019/166092 (in English)

Pakhomova, V. M., & Tsykalo, I. D. (2018). Optimal route definition in the network based on the multilayer neural model. Science and Transport Progress, 6(78), 126-142. DOI: https://doi.org/10.15802/stp2018/154443 (in English)

Pakhomova, V. M., & Mandybura, Y. S. (2019). Optimal route definition in the railway information network using neural-fuzzy models. Science and Transport Progress, 5(83), 81-98. DOI: https://doi.org/10.15802/stp2019/184385 (in English)

Sneps-Sneppe, M., & Namiot, D. (2020). Digital Railway and How to Move from GSM-R to LTE-R and 5G. Convergent Cognitive Information Technologies, 392-402. DOI: https://doi.org/10.1007/978-3-030-37436-5_34 (in English)

Vaishali, S. Nature-All-Mathematics.Retrieved from http://thebridge.psgtech.ac.in/index.php/2014/11/12/nature-all-mathematics

Ying, Tan. (2018). Survey of swarm intelligence. Swarm Intelligence-Vol. 1: Principles, current algorithms and methods, 1-28. DOI: https://doi.org/10.1049/pbce119f_ch1 (in English)

Zhong, Z.-D., Ai, B., Zhu, G., Wu, H., Xiong, L., Wang, F.-G., … & He, R.-S. (2017). Key Issues for GSM-R and LTE-R. Dedicated Mobile Communications for High-Speed Railway, 19-55. DOI: https://doi.org/10.1007/978-3-662-54860-8_2 (in English)

Zhukovyts’kyy, I., & Pakhomova, V. (2018). Research of Token Ring network options in automation system of marshalling yard. Transport Problems, 13(2), 149-158. DOI: https://doi.org/10.20858/tp.2018.13.2.1 (in English)


GOST Style Citations


  1. Скаков Е. С., Малыш В. Н. Пчелиный алгоритм оптимизации для решения задачи планирования беспроводной сети. Программные продукты и системы. 2016. № 4 (67). С. 67–73. DOI: https://doi.org/10.15827/0236-235X.115.067-073
  2. Смирнова О. С., Богорадникова А. В., Блинов М. Ю. Описание роевых алгоритмов, инспирированных неживой природой и бактериями, для использования в онтологической. International Journal of Open Information Technologies. 2015. Vol. 3, No. 12. С. 28–37.
  3. Ahamed A., Islam N., Soikot M. A. S., Hossen M. S., Ahmed R., Hasan M. A. Train Collision Avoidance
    Using GPS and GSM Module. 2019 International Conference on Power Electronics, Control and Automation (ICPECA). 2019. P. 1–4. DOI: https://doi.org/10.1109/icpeca47973.2019.8975543 
  4. Ai B., Guan K., Rupp M., Kurner T., Cheng X., Yin X.-F., … Ding J.-W. Future railway services-oriented mobile communications network. IEEE Communications Magazine. 2015. Vol. 53. Iss. 10. P. 78–85. DOI: https://doi.org/10.1109/MCOM.2015.7295467
  5. A Brief Overview of the Wireless World. URL: https://www.sciencedirect.com/topics/computer-science/basic-service-set. (date of access: 13.12.2019).
  6. A Necessary GSM-RMobileUpgrade. URL: https://www.railengineer.co.uk/2019/03/13/a-necessary-gsm-r-mobile-upgrade. (date of access: 13.12.2019).
  7. Banerjee S., Hempel N., Sharif H. A Survey of Wireless Communication Technologies & Their Performance for High Speed Railways. Journal of Transportation Technologies. 2016. Vol. 06. Iss. 01. P. 15–29. DOI: https://doi.org/10.4236/jtts.2016.61003
  8. Davidovic T., Teodorovic D., Selmic M. Bee Colony Optimization – Part I : The Algorithm Overview : Invited survey. YJOR. 2015. Vol. 25. Iss. 1. P. 33–56. DOI: https://doi.org/10.2298/YJOR131011017D
  9. Hussein W. A., Sahran S., Sheikh Abdullah S. N. H. The variants of the Bees Algorithm (BA) : A survey. Artificial Intelligence Review. 2017. Vol. 47. Iss. 1. P. 67–121. DOI: https://doi.org/10.1007/s10462-016-9476-8
  10. Kumar K., Zindani D., Davim J. P. Bees Algorithm. Optimizing Engineering Problems through Heuristic Techniques. 2019. P. 43-50. DOI: https://doi.org/10.1201/9781351049580-5
  11. Nurmi J., Lohan E.-S., Wymeersch H., Seco-Granados G., Nykänen O. Multi-Technology Positioning. Sprin-ger International Publishing AG. 2017. 348 p. DOI: https://doi.org/10.1007/978-3-319-50427-8
  12. Osterhage W. Wireless Network Security. Taylor & Francis Group. 2018. P. 14–77. DOI: https://doi.org/10.1201/9781315106373-3
  13. Pakhomova V. M., Skaballanovich T. I., Bondareva V. S. Intelligent routing in the network of information and telecommunication system of railway transport. Наука та прогрес транспорту. 2019. № 2 (80). P. 77–90. DOI: https://doi.org/10.15802/stp2019/166092
  14. Pakhomova V. M., Tsykalo I. D. Optimal route definition in the network based on the multilayer neural model. Наука та прогрес транспорту. 2018. № 6 (78). P. 126–142. DOI: https://doi.org/10.15802/stp2018/154443
  15. Pakhomova V. M., Mandybura Y. S. Optimal route definition in the railway information network using neural-fuzzy models. Наука та прогрес транспорту. 2019. № 5 (83). P. 81–98. DOI: https://doi.org/10.15802/stp2019/184385
  16. Sneps-Sneppe M., Namiot D. Digital Railway and How to Move from GSM-R to LTE-R and 5G. Convergent Cognitive Information Technologies. 2018. P. 392–402. DOI: https://doi.org/10.1007/978-3-030-37436-5_34 
  17. Vaishali S. Nature – All Mathematics. URL: http://thebridge.psgtech.ac.in/index.php/2014/11/12/nature-all-mathematics. (date of access: 13.12.2019).
  18. Ying Tan. Survey of swarm intelligence. Swarm Intelligence Vol. 1 : Principles, current algorithms and met-hods. 2018. P. 1–28. DOI: https://doi.org/10.1049/pbce119f_ch1
  19. Zhong Z.-D., Ai B., Zhu G., Wu H., Xiong L., Wang F.-G., … He R.-S. Key Issues for GSM-R and LTE-R. Dedicated Mobile Communications for High-speed Railway. 2018. P. 19–55. DOI: https://doi.org/10.1007/978-3-662-54860-8_2
  20. Zhukovyts’kyy I., Pakhomova V. Research of Token Ring network options in automation system of marshalling yard. Transport Problems. 2018. Vol. 13. Iss. 2. P. 149–158. DOI: https://doi.org/10.20858/tp.2018.13.2.14




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