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Performance Comparison of FC-RDVM and RDVM Router Replacement Methods by WMN-PSOHC Simulation System Considering Exponential Distribution of Mesh Clients

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 571))

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Abstract

Wireless Mesh Networks (WMNs) are low-cost and have easy maintenance. However, WMNs have some problems such as node placement problem, security, transmission power and so on. In this work, we deal with node placement problem. In our previous work, we implemented a hybrid simulation system based on Particle Swarm Optimization (PSO) and Hill Climbing (HC) called WMN-PSOHC for solving the node placement problem in WMNs. In this paper, we compare the performance of FC-RDVM with RDVM considering Exponential distribution of mesh clients. Simulation results show that FC-RDVM has better performance than RDVM.

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References

  1. 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 

  2. Ozera, K., Bylykbashi, K., Liu, Y., Barolli, L.: A fuzzy-based approach for cluster management in VANETs: performance evaluation for two fuzzy-based systems. Internet Things 3, 120–133 (2018)

    Article  Google Scholar 

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

    Google Scholar 

  4. Sakamoto, S., Lala, A., Oda, T., Kolici, V., Barolli, L., Xhafa, F.: Analysis of WMN-HC simulation system data using friedman test. In: The Ninth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2015), pp. 254–259. IEEE (2015)

    Google Scholar 

  5. 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 

  6. 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 

  7. Sakamoto, S., Barolli, A., Barolli, L., Okamoto, S.: Implementation of a web interface for hybrid intelligent systems. Int. J. Web Inf. Syst. 15(4), 420–431 (2019(a))

    Google Scholar 

  8. Sakamoto, S., Barolli, L., Okamoto, S.: WMN-PSOSA: an intelligent hybrid simulation system for WMNs and its performance evaluations. Int. J. Web Grid Serv. 15(4), 353–366 (2019(b))

    Google Scholar 

  9. Sakamoto, S., Barolli, L., Okamoto, S.: A comparison study of linearly decreasing inertia weight method and rational decrement of Vmax method for WMNs using WMN-PSOHC intelligent system considering normal distribution of mesh clients. In: Barolli, L., Natwichai, J., Enokido, T. (eds.) EIDWT 2021. LNDECT, vol. 65, pp. 104–113. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70639-5_10

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

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  13. 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 

  14. 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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Correspondence to Shinji Sakamoto .

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Sakamoto, S., Barolli, A., Liu, Y., Kulla, E., Barolli, L., Takizawa, M. (2023). Performance Comparison of FC-RDVM and RDVM Router Replacement Methods by WMN-PSOHC Simulation System Considering Exponential Distribution of Mesh Clients. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2022. Lecture Notes in Networks and Systems, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-031-19945-5_17

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  • DOI: https://doi.org/10.1007/978-3-031-19945-5_17

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  • Online ISBN: 978-3-031-19945-5

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