Performance Evaluation of WMNs by WMN-PSOSA Simulation System Considering Constriction and Linearly Decreasing Inertia Weight Methods

  • Shinji Sakamoto
  • Kosuke Ozera
  • Makoto Ikeda
  • Leonard Barolli
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 7)

Abstract

Wireless Mesh Networks (WMNs) have many advantages such as low cost and increased high speed wireless Internet connectivity, therefore WMNs are becoming an important networking infrastructure. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system for node placement in WMNs, called WMN-PSO. Also, we implemented a simulation system based on Simulated Annealing (SA) for solving node placement problem in WMNs, called WMN-SA. In this paper, we implement a hybrid simulation system based on PSO and SA, called WMN-PSOSA. We evaluate the performance of WMN-PSOSA by conducting computer simulations considering two replacement methods: Constriction Method (CM) and Linearly Decreasing Inertia Weight Method (LDIWM). Simulation results show that the LDIWM has better performance than CM.

Notes

Acknowledgement

This work is supported by a Grant-in-Aid for Scientific Research from Japanese Society for the Promotion of Science (JSPS KAKENHI Grant Number 15J12086). The authors would like to thank JSPS for the financial support.

References

  1. 1.
    Akyildiz, I.F., Wang, X., Wang, W.: Wireless mesh networks: a survey. Comput. Netw. 47(4), 445–487 (2005)CrossRefMATHGoogle Scholar
  2. 2.
    Amaldi, E., Capone, A., Cesana, M., Filippini, I., Malucelli, F.: Optimization models and methods for planning wireless mesh networks. Comput. Netw. 52(11), 2159–2171 (2008)CrossRefMATHGoogle Scholar
  3. 3.
    Barolli, A., Spaho, E., Barolli, L., Xhafa, F., Takizawa, M.: QoS routing in ad-hoc networks using GA and multi-objective optimization. Mob. Inf. Syst. 7(3), 169–188 (2011)Google Scholar
  4. 4.
    Behnamian, J., Ghomi, S.F.: Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting. Expert Syst. Appl. 37(2), 974–984 (2010)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Cunha, M.D.C., Sousa, J.: Water distribution network design optimization: simulated annealing approach. J. Water Resour. Plan. Manag. 125(4), 215–221 (1999)CrossRefGoogle Scholar
  7. 7.
    Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12(2), 171–195 (2008)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Ge, H., Du, W., Qian, F.: A hybrid algorithm based on particle swarm optimization and simulated annealing for job shop scheduling. In: Third International Conference on Natural Computation (ICNC-2007), vol. 3, pp. 715–719 (2007)Google Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Hiyama, M., Sakamoto, S., Kulla, E., Ikeda, M., Barolli, L.: Experimental results of a MANET testbed for different settings of HELLO packets of OLSR protocol. J. Mob. Multimedia 9(1–2), 27–38 (2013)Google Scholar
  13. 13.
    Hwang, C.R.: Simulated annealing: theory and applications. Acta Applicandae Mathematicae 12(1), 108–111 (1988)Google Scholar
  14. 14.
    Inaba, T., Elmazi, D., Liu, Y., Sakamoto, S., Barolli, L., Uchida, K.: Integrating wireless cellular and ad-hoc networks using fuzzy logic considering node mobility and security. In: The 29th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA-2015), pp. 54–60 (2015). doi:10.1109/WAINA.2015.116
  15. 15.
    Inaba, T., Elmazi, D., Sakamoto, S., Oda, T., Ikeda, M., Barolli, L.: A secure-aware call admission control scheme for wireless cellular networks using fuzzy logic and its performance evaluation. J. Mob. Multimedia 11(3&4), 213–222 (2015)Google Scholar
  16. 16.
    Inaba, T., Sakamoto, S., Kulla, E., Caballe, S., Ikeda, M., Barolli, L.: An integrated system for wireless cellular and ad-hoc networks using fuzzy logic. In: International Conference on Intelligent Networking and Collaborative Systems (INCoS-2014), pp. 157–162 (2014)Google Scholar
  17. 17.
    Inaba, T., Sakamoto, S., Oda, T., Ikeda, M., Barolli, L.: A testbed for admission control in WLAN: a fuzzy approach and its performance evaluation. In: International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 559–571. Springer (2016)Google Scholar
  18. 18.
    Lim, A., Rodrigues, B., Wang, F., Xu, Z.: k-Center problems with minimum coverage. In: Chwa, K.Y., Munro, J.I.J. (eds.) Computing and Combinatorics, pp. 349–359. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Maolin, T., et al.: Gateways placement in backbone wireless mesh networks. Int. J. Commun. Netw. Syst. Sci. 2(1), 44 (2009)Google Scholar
  20. 20.
    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
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  23. 23.
    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. Multimedia 9(1–2), 101–110 (2013)Google Scholar
  24. 24.
    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)Google Scholar
  25. 25.
    Sakamoto, S., Kulla, E., Oda, T., Ikeda, M., Barolli, L., Xhafa, F.: Performance evaluation considering iterations per phase and SA temperature in WMN-SA system. Mob. Inf. Syst. 10(3), 321–330 (2014)Google Scholar
  26. 26.
    Sakamoto, S., Lala, A., Oda, T., Kolici, V., Barolli, L., Xhafa, F.: Application of WMN-SA simulation system for node placement in wireless mesh networks: a case study for a realistic scenario. Int. J. Mob. Comput. Multimedia Commun. (IJMCMC) 6(2), 13–21 (2014)CrossRefGoogle Scholar
  27. 27.
    Sakamoto, S., Oda, T., Ikeda, M., Barolli, L., Xhafa, F.: An integrated simulation system considering wmn-pso simulation system and network simulator 3. In: International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 187–198. Springer (2016)Google Scholar
  28. 28.
    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)CrossRefGoogle Scholar
  29. 29.
    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). doi:10.1109/AINA.2016.42
  30. 30.
    Sakamoto, S., Oda, T., Ikeda, M., Barolli, L., Xhafa, F., Woungang, I.: Investigation of fitness function weight-coefficients for optimization in WMN-PSO simulation system. In: 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2016), pp. 224–229 (2016)Google Scholar
  31. 31.
    Schutte, J.F., Groenwold, A.A.: A study of global optimization using particle swarms. J. Global Optim. 31(1), 93–108 (2005)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Shi, Y.: Particle swarm optimization. IEEE Connections 2(1), 8–13 (2004)Google Scholar
  33. 33.
    Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) Evolutionary Programming VII, pp. 591–600. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  34. 34.
    Vanhatupa, T., Hannikainen, M., Hamalainen, T.: Genetic algorithm to optimize node placement and configuration for WLAN planning. In: Proceedings of 4th IEEE International Symposium on Wireless Communication Systems, pp. 612–616 (2007)Google Scholar
  35. 35.
    Wang, J., Xie, B., Cai, K., Agrawal, D.P.: Efficient mesh router placement in wireless mesh networks. In: Proceedings of Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS-2007), pp. 1–9 (2007)Google Scholar
  36. 36.
    Xhafa, F., Sanchez, C., Barolli, L.: Ad hoc and neighborhood search methods for placement of mesh routers in wireless mesh networks. In: Proceedings of 29th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS-2009), pp. 400–405 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Shinji Sakamoto
    • 1
  • Kosuke Ozera
    • 1
  • Makoto Ikeda
    • 2
  • Leonard Barolli
    • 2
  1. 1.Graduate School of EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan
  2. 2.Department of Information and Communication EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan

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