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Performance Evaluation of WMN-PSOSA Considering Four Different Replacement Methods

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Advances in Internet, Data & Web Technologies (EIDWT 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 17))

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 four different replacement methods. The simulation results show that LDIWM have better performance than CM, RIWM and LDVM replacement methods.

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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. Plann. Manag. 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. Hwang, C.R.: Simulated annealing: theory and applications. Acta Applicandae Mathematicae 12(1), 108–111 (1988)

    Google Scholar 

  13. 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 

  14. Inaba, T., Obukata, R., Sakamoto, S., Oda, T., Ikeda, M., Barolli, L.: Performance evaluation of a QoS-aware fuzzy-based CAC for LAN access. Int. J. Space Based Situated Comput. 6(4), 228–238 (2016)

    Article  Google Scholar 

  15. 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 

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

    Google Scholar 

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

    Google Scholar 

  18. 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 

  19. 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 

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

    Article  Google Scholar 

  21. 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 

  22. 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 

  23. Sakamoto, S., Kulla, E., Oda, T., Ikeda, M., Barolli, L., Xhafa, F.: A simulation system for WMN based on SA: performance evaluation for different instances and starting temperature values. Int. J. Space Based Situated Comput. 4(3–4), 209–216 (2014)

    Article  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). https://doi.org/10.1109/AINA.2016.42

  29. Sakamoto, S., Obukata, R., Oda, T., Barolli, L., Ikeda, M., Barolli, A.: Performance analysis of two wireless mesh network architectures by WMNSA and WMN-TS simulation systems. J. High Speed Netw. 23(4), 311–322 (2017)

    Article  Google Scholar 

  30. Sakamoto, S., Ozera, K., Barolli, A., Ikeda, M., Barolli, L., Takizawa, M.: Implementation of an intelligent hybrid simulation systems for WMNs based on particle swarm optimization and simulated annealing: performance evaluation for different replacement methods. Soft Comput. (2017). http://doi.org/10.1007/s00500-017-2948-1. Accessed 11 Dec 2017

  31. 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. (2017). http://doi.org/10.1007/s11036-017-0897-7. Accessed 06 Sep 2017

  32. 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 

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

    Google Scholar 

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

    Google Scholar 

  35. 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 

  36. 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 

  37. 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, 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Shinji Sakamoto & Kosuke Ozera

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

    Admir Barolli

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

    Leonard Barolli

  4. Faculty of Information Technology, Polytechnic University of Tirana, Mother Teresa Square, No. 4, Tirana, Albania

    Vladi Kolici

  5. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Kajino-Machi, Koganei-Shi, Tokyo, 184-8584, Japan

    Makoto Takizawa

Authors
  1. Shinji Sakamoto
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  2. Kosuke Ozera
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  3. Admir Barolli
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  4. Leonard Barolli
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  5. Vladi Kolici
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  6. Makoto Takizawa
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Corresponding author

Correspondence to Shinji Sakamoto .

Editor information

Editors and Affiliations

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

    Leonard Barolli

  2. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Nadeem Javaid

  4. Polytechnic University of Tirana, Tirana, Albania

    Evjola Spaho

  5. Polytechnic University of Tirana, Tirana, Albania

    Vladi Kolici

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Sakamoto, S., Ozera, K., Barolli, A., Barolli, L., Kolici, V., Takizawa, M. (2018). Performance Evaluation of WMN-PSOSA Considering Four Different Replacement Methods. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-75928-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75927-2

  • Online ISBN: 978-3-319-75928-9

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