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Effects of population size for location-aware node placement in WMNs: evaluation by a genetic algorithm--based approach

Personal and Ubiquitous Computing volume 18, pages 261–269 (2014)Cite this article

Abstract

Wireless mesh networks (WMNs) are cost-efficient networks that have the potential to serve as an infrastructure for advanced location-based services. Location service is a desired feature for WMNs to support location-oriented applications. WMNs are also interesting infrastructures for supporting ubiquitous multimedia Internet access for mobile or fixed mesh clients. In order to efficiently support such services and offering QoS, the optimized placement of mesh router nodes is very important. Indeed, such optimized mesh placement can support location service managed in the mesh and keep the rate of location updates low. This node location-based problem has been shown to be NP-hard and thus is unlikely to be solvable in reasonable amount of time. Therefore, heuristic methods, such as genetic algorithms (GAs), are used as resolution methods. In this paper, we deal with the effect of population size for location-aware node placement in WMNs. Our WMN-GA system uses GA to determine the positions of the mesh routers and mesh clients in the grid area. We used a location-aware node placement of mesh router in cells of considered grid area to maximize network connectivity and user coverage. We evaluate the performance of the proposed and implemented WMN-GA system for low and high density of clients considering different distributions and considering giant component and number of covered users parameters. The simulation results show that for low-density networks, with the increasing of population size, GA obtains better result. However, with the increase in the population size, the GA needs more computational time. The proposed system has better performance in dense networks like hot spots for Weibull distribution when the population size is big.

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References

  1. Weiser M (1993) Some computer science issues in ubiquitous computing. Symposium on Theory of computing, May 16–18, San Diego, California, pp 422–430

  2. Chung T, Chang H, Lee HG (2011) A novel cross-layer mesh router placement scheme for wireless mesh networks, EURASIP J Wirel Commun Netw doi:10.1186/1687-1499-2011-134, http://jwcn.eurasipjournals.com/content/2011/1/134

  3. Akyildiz IF, Wang X, Wang W (2005) Wireless mesh networks: a survey. Comput Netw 47(4):445–487

    Google Scholar 

  4. Nandiraju N, Nandiraju D, Santhanama L, He B, Wang J, Agrawal D (2007) Wireless mesh networks: current challenges and future direction of web-in-the-sky. IEEE Wirel Commun 79–89

  5. Rousseau F, Grunenberger Y, Untz V, Schiller E, Starzetz P, Theoleyre F, Heusse M, Alphand O, Duda A (2007) An architecture for seamless mobility in spontaneous wireless mesh networks. In: Proceedings of the 2nd ACM/IEEE international workshop on mobility in the evolving internet architecture (Mobiarch07), Kyoto, Japan, August 27, http://drakkar.imag.fr/spip.php?article23&lang=fr

  6. Chen Ch, Chekuri Ch (2007) Urban wireless mesh network planning: the case of directional antennas. Tech Report No. UIUCDCS-R-2007-2874, Department of Computer Science, University of Illinois at Urbana-Champaign

  7. Garey MR, Johnson DS (1979) Computers and intractability—A guide to the theory of NP-completeness. Freeman, San Francisco

    MATH  Google Scholar 

  8. Lim A, Rodrigues B, Wang F, Xua Zh (2005) k − center problems with minimum coverage. Theor Comput Sci 332(1–3):1–17

    Article  MATH  Google Scholar 

  9. Amaldi E, Capone A, Cesana M, Filippini I, Malucelli F (2008) Optimization models and methods for planning wireless mesh networks. Comput Netw 52:2159–2171

    Article  MATH  Google Scholar 

  10. Wang J, Xie B, Cai K, Agrawal DP (2007) Efficient mesh router placement in wireless mesh networks. MASS-2007, Pisa, Italy, pp 9–11

  11. Muthaiah SN, Rosenberg C (2008) Single gateway placement in wireless mesh networks. In: Proceedings of 8th international IEEE symposium on computer networks, Turkey, pp 4754–4759

  12. Zhou P, Manoj BS, Rao R (2007) A gateway placement algorithm in wireless mesh networks. In: Proceedings of the 3rd international conference on wireless internet (Austin, Texas, October 22–24, 2007). ICST (Institute for Computer Sciences Social-Informatics and Telecommunications Engineering), Brussels, Belgium, pp 1–9

  13. Tang M (2009) Gateways placement in backbone wireless mesh networks. Int J Commun Netw Syst Sci 2(1):45–50

    Google Scholar 

  14. Antony Franklin A, Siva Ram Murthy C (2007) Node placement algorithm for deployment of two-tier wireless mesh networks. In: Proceedings of IEEE GLOBECOM-2007, Washington, USA, pp 4823–4827

  15. Vanhatupa T, Hännikäinen M, Hämäläinen TD (2007) Genetic algorithm to optimize node placement and configuration for WLAN planning. In: Proceedings of 4th international symposium on wireless communication systems, pp 612–616

  16. Weinschrott H, Dürr F, Rothermel K (2010) Symbolic routing for location-based services in wireless mesh networks. In: Proceedings of AINA 2010, pp 851–858

  17. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  18. Xhafa F, Sanchez Ch, Barolli L (2010) Genetic algorithms for efficient placement of router nodes in wireless mesh networks. In: Proceedings of AINA 2010, pp 465–472

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Acknowledgments

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.

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Authors and Affiliations

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

    Tetsuya Oda & Evjola Spaho

  2. Department of Computers and Information Science, Seikei University, 3-3-1 Kichijoji-Kitamachi, Musashino-Shi, Tokyo, 180-8633, Japan

    Admir Barolli

  3. Department of Languages and Informatics Systems Technical, University of Catalonia, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

  4. Department of Computing and Communication Technologies, Oxford Brookes University, Oxford, OX33 1HX, UK

    Muhammad Younas

  5. Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), Japan, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0295, Japan

    Leonard Barolli

Authors
  1. Tetsuya Oda
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  2. Admir Barolli
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  3. Evjola Spaho
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  4. Leonard Barolli
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  5. Fatos Xhafa
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  6. Muhammad Younas
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Correspondence to Evjola Spaho.

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Oda, T., Barolli, A., Spaho, E. et al. Effects of population size for location-aware node placement in WMNs: evaluation by a genetic algorithm--based approach. Pers Ubiquit Comput 18, 261–269 (2014). https://doi.org/10.1007/s00779-013-0643-5

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  • DOI: https://doi.org/10.1007/s00779-013-0643-5

Keywords

  • Wireless mesh networks
  • Genetic algorithms
  • Selection operators
  • Size of giant component
  • Number of covered users