Skip to main content
SpringerLink
Log in

Mesh Routers Placement by WMN-PSODGA Hybrid Intelligent System Considering Stadium Distribution and RDVM: A Comparison Study for Different Crossover Methods

  • Conference paper
  • First Online:
Advances in Intelligent Networking and Collaborative Systems (INCoS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 527))

Abstract

Designing a robust Wireless Mesh Network (WMN) at low cost requires the use of the least possible mesh routers but still interconnected and able to offer full coverage. Therefore, the placement of mesh routers over the area of interest is a problem that entails thorough planning. In our previous work, we implemented a simulation system that deals with this problem by considering Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA), called WMN-PSODGA. In this paper, we compare the results of Stadium distribution of mesh clients for Unimodal Normal Distribution Crossover (UNDX), Multi-parental UNDX (UNDX-m), and Simplex Crossover (SPX) methods with Rational Decrement of Vmax Method (RDVM) as a router replacement method. The simulation results show that UNDX achieves full client coverage, better connectivity and improved load balance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Akyildiz, I.F., Wang, X., Wang, W.: Wireless mesh networks: a survey. Comput. Netw. 47(4), 445–487 (2005)

    Article  Google Scholar 

  2. Barolli, A., Sakamoto, S., Barolli, L., Takizawa, M.: Performance analysis of simulation system based on particle swarm optimization and distributed genetic algorithm for WMNs considering different distributions of mesh clients. In: Barolli, L., Xhafa, F., Javaid, N., Enokido, T. (eds.) IMIS 2018. AISC, vol. 773, pp. 32–45. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93554-6_3

    Chapter  Google Scholar 

  3. Barolli, A., Sakamoto, S., Ozera, K., Barolli, L., Kulla, E., Takizawa, M.: Design and implementation of a hybrid intelligent system based on particle swarm optimization and distributed genetic algorithm. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds.) EIDWT 2018. LNDECT, vol. 17, pp. 79–93. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75928-9_7

    Chapter  Google Scholar 

  4. Barolli, A., Bylykbashi, K., Qafzezi, E., Sakamoto, S., Barolli, L., Takizawa, M.: Mesh routers placement by WMN-PSODGA simulation system considering stadium distribution and RDVM: a comparison study for UNDX and UNDX-M methods. In: Barolli, L. (ed.) Innovative Mobile and Internet Services in Ubiquitous Computing. LNCS, vol. 496, pp. 184–195. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08819-3_18

    Chapter  Google Scholar 

  5. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  6. Franklin, A.A., Murthy, C.S.R.: Node placement algorithm for deployment of two-tier wireless mesh networks. In: Proceedings of Global Telecommunications Conference, pp. 4823–4827 (2007)

    Google Scholar 

  7. Girgis, M.R., Mahmoud, T.M., Abdullatif, B.A., Rabie, A.M.: Solving the wireless mesh network design problem using genetic algorithm and simulated annealing optimization methods. Int. J. Comput. Appl. 96(11), 1–10 (2014)

    Google Scholar 

  8. Goto, K., Sasaki, Y., Hara, T., Nishio, S.: Data gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks. Mob. Inf. Syst. 9(4), 295–314 (2013)

    Google Scholar 

  9. Lim, A., Rodrigues, B., Wang, F., Xu, Z.: k-center problems with minimum coverage. Theoret. Comput. Sci. 332(1–3), 1–17 (2005)

    Article  MathSciNet  Google Scholar 

  10. Maolin, T., et al.: Gateways placement in backbone wireless mesh networks. Int. J. Commun. Netw. Syst. Sci. 2(1), 44–50 (2009)

    Google Scholar 

  11. Matsuo, K., Sakamoto, S., Oda, T., Barolli, A., Ikeda, M., Barolli, L.: Performance analysis of WMNs by WMN-GA simulation system for two WMN architectures and different TCP congestion-avoidance algorithms and client distributions. Int. J. Commun. Netw. Distrib. Syst. 20(3), 335–351 (2018)

    Google Scholar 

  12. Muthaiah, S.N., Rosenberg, C.P.: Single gateway placement in wireless mesh networks. In: Proceedings of 8th International IEEE Symposium on Computer Networks, pp. 4754–4759 (2008)

    Google Scholar 

  13. Naka, S., Genji, T., Yura, T., Fukuyama, Y.: A hybrid particle swarm optimization for distribution state estimation. IEEE Trans. Power Syst. 18(1), 60–68 (2003)

    Article  Google Scholar 

  14. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  15. Sakamoto, S., Kulla, E., Oda, T., Ikeda, M., Barolli, L., Xhafa, F.: A comparison study of simulated annealing and genetic algorithm for node placement problem in wireless mesh networks. J. Mob. Multimed. 9(1–2), 101–110 (2013)

    Google Scholar 

  16. Sakamoto, S., Kulla, E., Oda, T., Ikeda, M., Barolli, L., Xhafa, F.: A comparison study of hill climbing, simulated annealing and genetic algorithm for node placement problem in WMNs. J. High Speed Netw. 20(1), 55–66 (2014)

    Article  Google Scholar 

  17. Sakamoto, S., Oda, T., Ikeda, M., Barolli, L., Xhafa, F.: Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks. Int. J. Commun. Netw. Distrib. Syst. 17(1), 1–13 (2016)

    Google Scholar 

  18. Sakamoto, S., Oda, T., Ikeda, M., Barolli, L., Xhafa, F.: Implementation of a new replacement method in WMN-PSO simulation system and its performance evaluation. In: The 30th IEEE International Conference on Advanced Information Networking and Applications (AINA-2016), pp. 206–211 (2016)

    Google Scholar 

  19. Sakamoto, S., Ozera, K., Ikeda, M., Barolli, L.: Implementation of intelligent hybrid systems for node placement problem in WMNs considering particle swarm optimization, hill climbing and simulated annealing. Mob. Netw. Appl. 23(1), 27–33 (2017). https://doi.org/10.1007/s11036-017-0897-7

    Article  Google Scholar 

  20. Schutte, J.F., Groenwold, A.A.: A study of global optimization using particle swarms. J. Global Optim. 31(1), 93–108 (2005)

    Article  MathSciNet  Google Scholar 

  21. Shi, Y.: Particle swarm optimization. IEEE Connections 2(1), 8–13 (2004)

    Google Scholar 

  22. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0040810

    Chapter  Google Scholar 

  23. Vanhatupa, T., Hannikainen, M., Hamalainen, T.: Genetic algorithm to optimize node placement and configuration for WLAN planning. In: Proceedings of the 4th IEEE International Symposium on Wireless Communication Systems, pp. 612–616 (2007)

    Google Scholar 

  24. Wang, J., Xie, B., Cai, K., Agrawal, D.P.: Efficient mesh router placement in wireless mesh networks. In: Proceedings of IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS-2007), pp. 1–9 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Aleksander Moisiu University of Durres, L.1, Rruga e Currilave, Durres, Albania

    Admir Barolli

  2. Department of Information and Communication Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka, 811-0295, Japan

    Kevin Bylykbashi & Leonard Barolli

  3. Graduate School of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka, 811-0295, Japan

    Ermioni Qafzezi

  4. Department of Information and Computer Science, Kanazawa Institute of Technology, 7-1 Ohgigaoka, Nonoichi, Ishikawa, 921-8501, Japan

    Shinji Sakamoto

  5. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, 3-7-2, Kajino-machi, Koganei-shi, Tokyo, 184-8584, Japan

    Makoto Takizawa

Authors
  1. Admir Barolli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kevin Bylykbashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ermioni Qafzezi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shinji Sakamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leonard Barolli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Makoto Takizawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Admir Barolli .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Barolli, A., Bylykbashi, K., Qafzezi, E., Sakamoto, S., Barolli, L., Takizawa, M. (2022). Mesh Routers Placement by WMN-PSODGA Hybrid Intelligent System Considering Stadium Distribution and RDVM: A Comparison Study for Different Crossover Methods. In: Barolli, L., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2022. Lecture Notes in Networks and Systems, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-14627-5_19

Download citation

Publish with us

Policies and ethics

Search

Navigation