Skip to main content
SpringerLink
Log in

Performance Evaluation of WMNs by WMN-PSOHC Hybrid Simulation System Considering CM and LDVM Replacement Methods

  • Conference paper
  • First Online:
Advanced Information Networking and Applications (AINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1151))

  • 2072 Accesses

Abstract

With the rapid development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we 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 considering Constriction Method (CM) and Linearly Decreasing Vmax Method (LDVM). Simulation results show that a good performance is achieved for CM compared with LDVM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

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 Util. Comput. 10(1), 10–21 (2019)

    Article  Google Scholar 

  2. Akyildiz, I.F., Wang, X., Wang, W.: Wireless mesh networks: a survey. Comput. Netw. 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., Durresi, H., Ohara, S., Barolli, L., Takizawa, M.: A comparison study of constriction and linearly decreasing Vmax replacement methods for wireless mesh networks by WMN-PSOHC-DGA simulation system. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 26–34. Springer (2019)

    Google Scholar 

  7. 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 

  8. Barolli, A., Sakamoto, S., Ohara, S., Barolli, L., Takizawa, M.: Performance analysis of WMNs by WMN-PSOHC-DGA simulation system considering random inertia weight and linearly decreasing Vmax router replacement methods. In: Conference on Complex, Intelligent, and Software Intensive Systems, pp. 13–21. Springer (2019)

    Google Scholar 

  9. 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 

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

    Article  Google Scholar 

  12. 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 

  13. 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 

  14. 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, 100089 (2019)

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  17. 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. Netw. Distrib. Syst. 20(3), 335–351 (2018)

    Article  Google Scholar 

  18. 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 

  19. 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 

  20. 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 

  21. 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 Util. Comput. 10(2), 168–178 (2019)

    Article  Google Scholar 

  22. 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 

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

    Article  Google Scholar 

  24. 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 

  25. 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 

  26. 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

  27. 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 Netw. 23(4), 311–322 (2017)

    Article  Google Scholar 

  28. 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 Netw. Appl. 23(1), 27–33 (2018)

    Article  Google Scholar 

  29. 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 

  30. 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 

  31. Sakamoto, S., Ohara, S., Barolli, L., Okamoto, S.: Performance evaluation of WMNs by WMN-PSOHC system considering random inertia weight and linearly decreasing inertia weight replacement methods. In: International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 39–48. Springer (2019)

    Google Scholar 

  32. 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 

  33. 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 

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

    Google Scholar 

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

    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 

  38. 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 Util. Comput. 10(5), 497–511 (2019)

    Article  Google Scholar 

Download references

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

    Seiji Ohara

  3. 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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seiji Ohara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonard Barolli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shusuke Okamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shinji Sakamoto .

Editor information

Editors and Affiliations

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

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania “Luigi Vanvitelli”, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sakamoto, S., Ohara, S., Barolli, L., Okamoto, S. (2020). Performance Evaluation of WMNs by WMN-PSOHC Hybrid Simulation System Considering CM and LDVM Replacement Methods. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_27

Download citation

Publish with us

Policies and ethics

Search

Navigation