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Soft Computing

, Volume 23, Issue 9, pp 3029–3035 | Cite as

Implementation of an intelligent hybrid simulation systems for WMNs based on particle swarm optimization and simulated annealing: performance evaluation for different replacement methods

  • Shinji SakamotoEmail author
  • Kosuke Ozera
  • Admir Barolli
  • Makoto Ikeda
  • Leonard Barolli
  • Makoto Takizawa
Methodologies and Application

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) 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 the rational decrement of Vmax method and linearly decreasing inertia weight method have better performance compared with constriction method and random inertia weight method.

Keywords

Wireless mesh networks Node placement problem Particle swarm optimization Simulated annealing Intelligent hybrid system Replacement methods 

Notes

Acknowledgements

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.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

References

  1. Akyildiz IF, Wang X, Wang W (2005) Wireless mesh networks: a survey. Comput Netw 47(4):445–487CrossRefzbMATHGoogle Scholar
  2. Amaldi E, Capone A, Cesana M, Filippini I, Malucelli F (2008) Optimization models and methods for planning wireless mesh networks. Comput Netw 52(11):2159–2171CrossRefzbMATHGoogle Scholar
  3. Barolli A, Spaho E, Barolli L, Xhafa F, Takizawa M (2011) QoS routing in ad-hoc networks using GA and multi-objective optimization. Mob Inf Syst 7(3):169–188Google Scholar
  4. Behnamian J, Ghomi SF (2010) Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting. Expert Syst Appl 37(2):974–984CrossRefGoogle Scholar
  5. Chen L, Liu L, Qi X, Zheng G (2017) Cooperation forwarding data gathering strategy of wireless sensor networks. Int J Grid Util Comput 8(1):46–52CrossRefGoogle Scholar
  6. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  7. Cunha MdC, Sousa J (1999) Water distribution network design optimization: simulated annealing approach. J Water Resour Plan Manag 125(4):215–221CrossRefGoogle Scholar
  8. Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez J-C, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195CrossRefGoogle Scholar
  9. Franklin AA, Murthy CSR (2007) Node placement algorithm for deployment of two-tier wireless mesh networks. In Proceedings of global telecommunications conference, pp 4823–4827Google Scholar
  10. Ge H, Du W, Qian F (2007) 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 715–719Google Scholar
  11. Girgis MR, Mahmoud TM, Abdullatif BA, Rabie AM (2014) Solving the wireless mesh network design problem using genetic algorithm and simulated annealing optimization methods. Int J Comput Appl 96(11):1–10Google Scholar
  12. Goto K, Sasaki Y, Hara T, Nishio S (2013) Data gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks. Mob Inf Syst 9(4):295–314Google Scholar
  13. Gupta BK, Patnaik S, Mallick MK, Nayak AK (2017) Dynamic routing algorithm in wireless mesh network. Int J Grid Util Comput 8(1):53–60CrossRefGoogle Scholar
  14. Hiyama M, Sakamoto S, Kulla E, Ikeda M, Barolli L (2013) Experimental results of a MANET testbed for different settings of HELLO packets of OLSR protocol. J Mob Multimed 9(1–2):27–38Google Scholar
  15. Houaidia C, Idoudi H, Van Den Bossche A, Val T, Saidane LA (2014) Towards an optimised traffic-aware routing in wireless mesh networks. Int J Space Based Situat Comput 4(3–4):217–232CrossRefGoogle Scholar
  16. Hwang C-R (1988) Simulated annealing: theory and applications. Acta Appl Math 12(1):108–111Google Scholar
  17. Inaba T, Elmazi D, Liu Y, Sakamoto S, Barolli L, Uchida K (2015a) 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 54–60Google Scholar
  18. Inaba T, Elmazi D, Sakamoto S, Oda T, Ikeda M, Barolli L (2015) A secure-aware call admission control scheme for wireless cellular networks using fuzzy logic and its performance evaluation. J Mob Multimed 11(3&4):213–222Google Scholar
  19. Inaba T, Sakamoto S, Kulla E, Caballe S, Ikeda M, Barolli L (2014) 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 157–162Google Scholar
  20. Inaba T, Sakamoto S, Oda T, Ikeda M, Barolli L (2016) 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, Springer, pp 559–571Google Scholar
  21. Lim A, Rodrigues B, Wang F, Xu Z (2004) k-Center problems with minimum coverage. In: Computing and combinatorics, pp 349–359Google Scholar
  22. Maolin T et al (2009) Gateways placement in backbone wireless mesh networks. Int J Commun Netw Syst Sci 2(1):44Google Scholar
  23. Muthaiah SN, Rosenberg CP (2008) Single gateway placement in wireless mesh networks. In: Proceedings of 8th international IEEE symposium on computer networks, pp 4754–4759Google Scholar
  24. Naka S, Genji T, Yura T, Fukuyama Y (2003) A hybrid particle swarm optimization for distribution state estimation. IEEE Trans Power Syst 18(1):60–68CrossRefGoogle Scholar
  25. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
  26. Sakamoto S, Kulla E, Oda T, Ikeda M, Barolli L, Xhafa F (2013) A comparison study of simulated annealing and genetic algorithm for node placement problem in wireless mesh networks. J Mob Multimed 9(1–2):101–110Google Scholar
  27. Sakamoto S, Kulla E, Oda T, Ikeda M, Barolli L, Xhafa F (2014a) A comparison study of hill climbing simulated annealing and genetic algorithm for node placement problem in WMNs. J High Speed Netw 20(1):55–66Google Scholar
  28. Sakamoto S, Kulla E, Oda T, Ikeda M, Barolli L, Xhafa F (2014b) Performance evaluation considering iterations per phase and SA temperature in WMN-SA system. Mob Inf Syst 10(3):321–330Google Scholar
  29. Sakamoto S, Kulla E, Oda T, Ikeda M, Barolli L, Xhafa F (2014c) A simulation system for WMN based on SA: performance evaluation for different instances and starting temperature values. Int J Space Based Situat Comput 4(3–4):209–216CrossRefGoogle Scholar
  30. Sakamoto S, Lala A, Oda T, Kolici V, Barolli L, Xhafa F (2014d) Application of WMN-SA simulation system for node placement in wireless mesh networks: a case study for a realistic scenario. Int J Mob Comput Multimed Commun 6(2):13–21CrossRefGoogle Scholar
  31. Sakamoto S, Oda T, Ikeda M, Barolli L, Xhafa F (2016a) An integrated simulation system considering WMN-PSO simulation system and network simulator 3. In: International conference on broadband and wireless computing, communication and applications, Springer, pp 187–198Google Scholar
  32. Sakamoto S, Oda T, Ikeda M, Barolli L, Xhafa F (2016b) 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):1–13CrossRefGoogle Scholar
  33. Sakamoto S, Oda T, Ikeda M, Barolli L, Xhafa F (2016c) 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 206–211Google Scholar
  34. Schutte JF, Groenwold AA (2005) A study of global optimization using particle swarms. J Global Optim 31(1):93–108MathSciNetCrossRefzbMATHGoogle Scholar
  35. Shi Y (2004) Particle swarm optimization. IEEE Connect 2(1):8–13Google Scholar
  36. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: Evolutionary programming VII, pp 591–600Google Scholar
  37. Vanhatupa T, Hannikainen M, Hamalainen T (2007) Genetic algorithm to optimize node placement and configuration for WLAN planning. In: Proceedings of 4th IEEE international symposium on wireless communication systems, pp 612–616Google Scholar
  38. Wang J, Xie B, Cai K, Agrawal DP (2007) Efficient mesh router placement in wireless mesh networks. In: Proceedings of IEEE international conference on mobile adhoc and sensor systems (MASS-2007), pp 1–9Google Scholar
  39. Xhafa F, Sanchez C, Barolli L (2009) 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 400–405Google Scholar
  40. Yerra RVP, Rajalakshmi P (2014) Effect of relay nodes and transmit power on end-to-end delay in multi-hop wireless ad hoc networks. Int J Space Based Situat Comput 9 4(1):26–38CrossRefGoogle Scholar
  41. Zhang J, Yang T, Zhao C (2016) Energy-efficient and self-adaptive routing algorithm based on event-driven in wireless sensor network. Int J Grid Util Comput 7(1):41–49CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Graduate School of EngineeringFukuoka Institute of TechnologyFukuokaJapan
  2. 2.Department of Information TechnologyAleksander Moisiu University of Durres, L.1, Rruga e CurrilaveDurresAlbania
  3. 3.Department of Information and Communication EngineeringFukuoka Institute of TechnologyFukuokaJapan
  4. 4.Department of Advanced SciencesFaculty of Science and Engineering, Hosei UniversityTokyoJapan

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