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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

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Advances in Internet, Data and Web Technologies (EIDWT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 65))

Abstract

Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure. However, they have some problems such as node placement, security, transmission power and so on. To solve node placement problem in WMNs, we have implemented a hybrid simulation system based on PSO and HC called WMN-PSOHC. In this paper, we present the performance evaluation of WMNs by using WMN-PSOHC intelligent system considering Linearly Decreasing Inertia Weight Method (LDIWM) and Rational Decrement of Vmax Method (RDVM). The simulation results show that a better performance is achieved for LDIWM compared with the RDVM.

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References

  1. Ahmed, S., Khan, M.A., Ishtiaq, A., Khan, Z.A., Ali, M.T.: Energy harvesting techniques for routing issues in wireless sensor networks. Int. J. Grid Utility Comput. 10(1), 10–21 (2019)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Barolli, A., Sakamoto, S., Barolli, L., Takizawa, M.: A hybrid simulation system based on particle swarm optimization and distributed genetic algorithm for WMNs: performance evaluation considering normal and uniform distribution of mesh clients. In: International Conference on Network-Based Information Systems, pp. 42–55, Springer (2018)

    Google Scholar 

  4. 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: International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 32–45, Springer (2018)

    Google Scholar 

  5. Barolli, A., Sakamoto, S., Barolli, L., Takizawa, M.: Performance evaluation of WMN-PSODGA system for node placement problem in WMNs considering four different crossover methods. In: The 32nd IEEE International Conference on Advanced Information Networking and Applications (AINA-2018), pp 850–857. IEEE (2018)

    Google Scholar 

  6. Barolli, A., Sakamoto, S., Ohara, S., Barolli, L., Takizawa, M.: Performance analysis of WMNs by WMN-PSOHC-DGA simulation system considering linearly decreasing inertia weight and linearly decreasing Vmax replacement methods. In: International Conference on Intelligent Networking and Collaborative Systems, pp. 14–23, Springer (2019)

    Google Scholar 

  7. 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 

  8. 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 

  9. Gorrepotu, R., Korivi, N.S., Chandu, K., Deb, S.: Sub-1 GHz miniature wireless sensor node for IoT applications. Internet of Things 1, 27–39 (2018)

    Article  Google Scholar 

  10. 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 

  11. 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, Communication and Applications (2016)

    Google Scholar 

  12. Islam, M.M., Funabiki, N., Sudibyo, R.W., Munene, K.I., Kao, W.C.: A dynamic access-point transmission power minimization method using pi feedback control in elastic WLAN system for IoT applications. Internet of Things 8(100), 089 (2019)

    Google Scholar 

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

    Google Scholar 

  14. Marques, B., Coelho, I.M., Sena, A.D.C., Castro, M.C.: A network coding protocol for wireless sensor fog computing. Int. J. Grid Utility Comput. 10(3), 224–234 (2019)

    Article  Google Scholar 

  15. 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. Networks Distrib. Syst. 20(3), 335–351 (2018)

    Article  Google Scholar 

  16. 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 

  17. Ohara, S., Barolli, A., Sakamoto, S., Barolli, L.: Performance analysis of WMNs by WMN-PSODGA simulation system considering load balancing and client uniform distribution. In: International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 25–38, Springer (2019)

    Google Scholar 

  18. 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 of Things 3, 120–133 (2018)

    Article  Google Scholar 

  19. Ozera, K., Inaba, T., Bylykbashi, K., Sakamoto, S., Ikeda, M., Barolli, L.: A WLAN triage testbed based on fuzzy logic and its performance evaluation for different number of clients and throughput parameter. Int. J. Grid Utility Comput. 10(2), 168–178 (2019)

    Article  Google Scholar 

  20. Petrakis, E.G., Sotiriadis, S., Soultanopoulos, T., Renta, P.T., Buyya, R., Bessis, N.: Internet of Things as a service (iTaaS): challenges and solutions for management of sensor data on the cloud and the fog. Internet of Things 3, 156–174 (2018)

    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., 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 

  23. 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. Networks Distrib. Syst. 17(1), 1–13 (2016)

    Article  Google Scholar 

  24. 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. 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

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

    Article  Google Scholar 

  26. 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. Mobile Networks Appl. 23(1), 27–33 (2018)

    Article  Google Scholar 

  27. 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)

    Article  Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. 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. 23(9), 3029–3035 (2019)

    Article  Google Scholar 

  30. 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 

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

    Google Scholar 

  32. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. Evol. Program. VII 591–600 (1998)

    Google Scholar 

  33. 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 Ad hoc 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 

  35. Yaghoobirafi, K., Nazemi, E.: An autonomic mechanism based on ant colony pattern for detecting the source of incidents in complex enterprise systems. Int. J. Grid Utility Comput. 10(5), 497–511 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Seikei University, 3-3-1 Kichijoji-Kitamachi, Musashino-shi, Tokyo, 180-8633, Japan

    Shinji Sakamoto & Shusuke Okamoto

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

    Leonard Barolli

Authors
  1. Shinji Sakamoto
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  2. Leonard Barolli
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  3. Shusuke Okamoto
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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, Japan

    Leonard Barolli

  2. Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  3. Rissho University, Tokyo, Japan

    Tomoya Enokido

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Sakamoto, S., Barolli, L., Okamoto, S. (2021). 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) Advances in Internet, Data and Web Technologies. EIDWT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 65. Springer, Cham. https://doi.org/10.1007/978-3-030-70639-5_10

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