Advertisement

Multilevel Optimization Applied to Project of Access Networks for Implementation of Intelligent Cities

  • Márcio Joel BarthEmail author
  • Leandro Mengue
  • José Vicente Canto dos Santos
  • Juarez Machado da Silva
  • Marcelo Josué Telles
  • Jorge Luis Victória Barbosa
Chapter
First Online:
  • 437 Downloads
Part of the Urban Computing book series (UC)

Abstract

Studies about network infrastructure have been realized and applied in a high variety of service-based industries, these studies are currently being used to design the network infrastructure in smart cities. However, planning network infrastructure in different levels is a big problem to be solved, because, generally, literature presents solutions where just one level is processed and the problems are solved individually. Planning the distribution and connection of equipment at various levels of a network infrastructure is an arduous task, it is necessary to evaluate the quantity and the best geographical distribution of equipment at each level of the network. This research presents a metaheuristic inspired by the concepts of the genetic algorithms. The proposed paper can search for solutions to plan the network infrastructure of multilevel capacitated networks, solving the network planning problem and obtaining results that are 20% better at cost when compared with other solutions.

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

References

  1. 1.
    Agrawal, G.P.: Sistemas de Comunicação Por Fibra Óptica. Elsevier, São Paulo (2014)Google Scholar
  2. 2.
    Chiu, P.L., Lin, F.Y.S.: A simulated anneling algorithm to support the sensor placement for target location. In: Canadian Conference on Electrical and Computer Engineering 2004. IEEE, Piscataway (2004)Google Scholar
  3. 3.
    Floyd, R.W.: Algorithm 97: shortest path. Commun. ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  4. 4.
    Gamvros, I., Raghavan, S., Golden, B.: An evolutionary approach to the multi-level capacitated minimum spanning tree problem. In: Telecommunications Network Design and Management, pp. 99–124. Springer, Berlin (2002)Google Scholar
  5. 5.
    Goldbarg, M.C., Luna, H.P.L.: Otimização Combinatória e Programação Linear: Modelos e Algoritmos. Campus, Rio de Janeiro (2000)Google Scholar
  6. 6.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Adisson-Wesley, Reading (1989)Google Scholar
  7. 7.
    Grennsmith, J., Withbrook, A., Aickelin, U.: Handbook of Metaheuristics. Springer, Berlin (2010)Google Scholar
  8. 8.
    Gross, J.L., Yellen, J.: Graph Theory and Its Applications. Chapman & Hall, Boca Raton (2006)zbMATHGoogle Scholar
  9. 9.
    Kampstra, P., van der Mei, R.D., Eiben, A.E.: Evolutionary computing in telecommunication network design: a survey. In: Vrije Universiteit, Faculty of Exact Sciences and CWI, Advanced Communication Networks. Amsterdam (2006)Google Scholar
  10. 10.
    Lee, J.H., Hancock, M.G., Hu, M.C.: Towards an effective framework for building smart cities: lessons from Seoul and San Francisco. Technol. Forecast. Soc. Change 89, 80–99 (2013)CrossRefGoogle Scholar
  11. 11.
    Maia, L.P.: Arquitetura de Redes de Computadores. LTC, Rio de Janeiro (2013)Google Scholar
  12. 12.
    Martins, E.A.: Um Sistema Computacional Para Apoio a Projetos de Redes de Comunicação em Sistemas Centralizados de Medição de Consumo e Tarifação de Energia elétrica: Desenvolvimento e Implementação Através de uma Abordagem Metaheurística. UNISINOS, São Leopoldo (2013) (in Portuguese)Google Scholar
  13. 13.
    Monteiro, M.S.R., Fontes, D.B.M.M., Fontes, F.A.C.C.: The hop-constrained minimum cost flow spanning tree problem with nonlinear costs: an ant colony optimization approach. Optim. Lett. 9(3), 451–464 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Netto, P.O.B.: Grafos: Teoria, Modelos, Algoritmos. Blucher, São Paulo (2011)Google Scholar
  15. 15.
    Papadimitriou, C.H., Lewis, H.R.: Elementos de Teoria da Computação. Prentice Hall, São Paulo (1998)Google Scholar
  16. 16.
    Petrolo, R., Loscri, V., Mitton, N.: Towards a smart city based on cloud of things. In: Proceedings of the 2014 ACM Internacional Workshop on Wireless and Mobile Technologies for Smart Cities – WiMobCity 2014, pp. 61–66. ACM, New York (2014)Google Scholar
  17. 17.
    Piro, G., Cianci, I., Grieco, L.A., Boggia, G., Camarda, P.: Information centric services in Smart Cities. J. Syst. Softw. 88(1), 169–188 (2014)CrossRefGoogle Scholar
  18. 18.
    Segarra, J., Sales, V., Prat, J.: Planning and designing FTTH networks: elements, tools and practical issues. In: International Conference on Transparent Optical Networks, pp. 1–6. IEEE, Piscataway (2012)Google Scholar
  19. 19.
    Silva, H.A.D., De Souza Britto, A., Oliveira, L.E.S.D., Koerich, A.L.: Network infrastructure design with a multilevel algorithm. Expert Syst. Appl. 40(9), 3471–3480 (2013)CrossRefGoogle Scholar
  20. 20.
    Sukode, S., Gite, P.S., Agrawal, H.: Context aware framework in IoT: a survey. Int. J. 4(1), 1–9 (2015)Google Scholar
  21. 21.
    Varvarigos, E.A., Christodoulopoulos, K.: Algorithmic aspects in planning fixed and flexible optical networks with emphasis on linear optimization and heuristic tecniques. J. Lightwave Technol. 32(4), 681–693 (2014)CrossRefGoogle Scholar
  22. 22.
    Watcharasitthiwat, K., Wardkein, P.: Reability optimization of topology communication network design using an improved ant colony optimization. Comput. Electr. Eng. 35(5), 730–747 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Márcio Joel Barth
    • 1
    Email author
  • Leandro Mengue
    • 1
  • José Vicente Canto dos Santos
    • 1
  • Juarez Machado da Silva
    • 1
  • Marcelo Josué Telles
    • 1
  • Jorge Luis Victória Barbosa
    • 1
  1. 1.University of Vale do Rio dos Sinos—UNISINOSSão LeopoldoBrazil

Personalised recommendations

Cite chapter

Buy options