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Performance Evaluation of WMN-PSOHC and WMN-PSO Simulation Systems for Node Placement in Wireless Mesh Networks: A Comparison Study

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Advances in Internetworking, Data & Web Technologies (EIDWT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

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 Hill Climbing (HC) for solving node placement problem in WMNs, called WMN-HC. In this paper, we implement a hybrid simulation system based on PSO and HC, called WMN-PSOHC. We compare WMN-PSO with WMN-PSOHC by conducting computer simulations. The simulation results show that the WMN-PSOHC has better performance than WMN-PSO.

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References

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

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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)

    Article  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. Cunha, M.C., Sousa, J.: Water distribution network design optimization: simulated annealing approach. J. Water Resour. Plan. Manage. 125(4), 215–221 (1999)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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 

  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. 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. 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, pp. 559–571. Springer (2016)

    Google Scholar 

  17. Lim, A., Rodrigues, B., Wang, F., Xu, Z.: k-center problems with minimum coverage. In: Computing and Combinatorics, pp. 349–359 (2004)

    Google Scholar 

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

    Google Scholar 

  19. 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 

  20. 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 

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

    Article  Google Scholar 

  22. 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 

  23. 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 

  24. 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 

  25. 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)

    Article  Google Scholar 

  26. 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 

  27. 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)

    Article  Google Scholar 

  28. 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

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  31. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Evolutionary Programming VII, pp. 591–600 (1998)

    Google Scholar 

  32. 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 

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

    Google Scholar 

  34. 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 

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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.

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Authors and Affiliations

  1. Graduate School of Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Shinji Sakamoto & Kosuke Ozera

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

    Tetsuya Oda, Makoto Ikeda & Leonard Barolli

Authors
  1. Shinji Sakamoto
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  2. Kosuke Ozera
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  3. Tetsuya Oda
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  4. Makoto Ikeda
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  5. Leonard Barolli
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Corresponding author

Correspondence to Shinji Sakamoto .

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Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Sakamoto, S., Ozera, K., Oda, T., Ikeda, M., Barolli, L. (2018). Performance Evaluation of WMN-PSOHC and WMN-PSO Simulation Systems for Node Placement in Wireless Mesh Networks: A Comparison Study. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-59463-7

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