Performance Evaluation of WMNs Using Hill Climbing Algorithm Considering Giant Component and Different Distributions

  • Xinyue Chang
  • Tetsuya Oda
  • Evjola Spaho
  • Makoto Ikeda
  • Leonard Barolli
  • Fatos Xhafa
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)


In this paper, we propose and implement a system based on Hill Climbing algorithm, called WMN-HC. We evaluate the performance of the proposed system by different scenarios using giant component and different distribution of mesh clients. We present some evaluation scenarios and show that the proposed approach has a good performance.


Wireless mesh networks Hill climbing Node placement Giant component 



This work is supported by a Grant-in-Aid for Scientific Research from Japanese Society for the Promotion of Science (JSPS). The authors would like to thank JSPS for the financial support.


  1. 1.
    Akyildiz FI, Wang X, Wang W (2005) Wireless mesh networks: a survey. Comput Netw 47(4):445–487Google Scholar
  2. 2.
    Franklin A, Murthy C (2007) Node placement algorithm for deployment of two-tier wireless mesh networks. In: IEEE GLOBECOM-2007, pp 4823–4827Google Scholar
  3. 3.
    Lim A, Rodrigues B, Wang F, Xua Zh (2005) k-Center problems with minimum coverage. Theor Comput Sci 332(1-3):1–17Google Scholar
  4. 4.
    Muthaiah NS, Rosenberg C (2008) Single gateway placement in wireless mesh networks. In: 8th international IEEE symposium on computer networks, pp 4754–4759Google Scholar
  5. 5.
    Tang M (2009) Gateways placement in backbone wireless mesh networks. Int J Commun Netw Syst Sci 2(1):45–50Google Scholar
  6. 6.
    Vanhatupa T, Hännikäinen M, Hämäläinen DT (2007) Genetic algorithm to optimize node placement and configuration for WLAN planning. In: 4th international symposium on wireless communication systems, pp 612–616Google Scholar
  7. 7.
    Wang J, Xie B, Cai K, Agrawal PD (2007) Efficient mesh router placement in wireless mesh networks. In: MASS-2007, Pisa, Italy, pp 9–11Google Scholar
  8. 8.
    Xhafa F, Barolli L, Durresi A (2007) An experimental study on genetic algorithms for resource allocation on grid systems. J Interconnect Netw 8(4):427–443Google Scholar
  9. 9.
    Xhafa F, Sanchez C, Barolli L (2009) Ad Hoc and neighborhood search methods for placement of mesh routers in wireless mesh networks. In: ICDCS workshops of the IEEE 29th international conference on distributed computing systems (ICDCS-2009), pp 400–405Google Scholar
  10. 10.
    Yao X (1993) An empirical study of genetic operators in genetic algorithms. In: 19th EUROMICRO symposium on microprocessing and microprogramming on open system design: hardware, software and applications, Elsevier Science Publishers, pp 707–714Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Xinyue Chang
    • 1
  • Tetsuya Oda
    • 1
  • Evjola Spaho
    • 1
  • Makoto Ikeda
    • 2
  • Leonard Barolli
    • 2
  • Fatos Xhafa
    • 3
  1. 1.Graduate School of EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan
  2. 2.Department of Information and Communication EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan
  3. 3.Department of Languages and Informatics SystemsTechnical University of CataloniaBarcelonaSpain

Personalised recommendations