Skip to main content

Advertisement

SpringerLink
Log in

Solving the location areas management problem with multi-objective evolutionary strategies

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The location management is one of the most important tasks in current Public Land Mobile Networks because of the number of mobile subscribers has increased exponentially over the last decade. That is why systems that automatically optimize the operations involved in the location management (subscriber location update and paging) are becoming more necessary. There are several works in which different metaheuristics have been applied to optimize the location management tasks. In these works, the objective functions of the location update and paging were linearly combined into a single objective function with the goal of optimizing these two tasks by using Single-Objective Optimization Algorithms. In this paper, in order to avoid the drawbacks associated with the linear aggregation of the objective functions, we have adapted and modified two Multi-objective Evolutionary Algorithms: Non-dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2. Furthermore, we have performed an in-depth analysis of the Location Areas scheme and its relation to the user’s call and mobility patterns. This study concludes that the location areas are as small as possible due to the fast increase of the paging cost, and that the cells with higher mobile activity are located in the center of its location area. Moreover, results show that our algorithms outperform the single-objective optimization algorithms proposed by other authors in the two most complex test networks, as well as the advantages of using a multi-objective approach.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Lin, Y.-B., & Chlamtac, I. (2008). Wireless and mobile network architectures. New York: John Wiley India Pvt Limited.

    Google Scholar 

  2. Agrawal, D. P., & Zeng, Q. A. (2010). Introduction to wireless and mobile systems (3rd ed.). Stamford: Cengage Learning.

    Google Scholar 

  3. Garg, V. (2010). Wireless communications & networking. The Morgan Kaufmann series in networking. San Francisco: Elsevier Science.

    Google Scholar 

  4. Lescuyer, P., Lucidarme, T. (2008). Evolved packet system (EPS): The LTE and SAE evolution of 3G UMTS. New York: Wiley Publishing.

  5. Kyamakya, K., & Jobmann, K. (2005). Location management in cellular networks: classification of the most important paradigms, realistic simulation framework, and relative performance analysis. IEEE Transactions on Vehicular Technology, 54, 687–708.

    Article  Google Scholar 

  6. Nowoswiat, D., & Milliken, G. (2013). Managing LTE core network signaling traffic. Techzine: Alcatel-Lucent.

    Google Scholar 

  7. Taheri, J., & Zomaya, A. (2007). A combined genetic-neural algorithm for mobility management. Journal of Mathematical Modeling and Algorithms, 6, 481–507.

    Article  MathSciNet  MATH  Google Scholar 

  8. Gondim, P. R. L. (1996). Genetic algorithms and the location area partitioning problem in cellular networks. In Proceedings of IEEE 46th vehicular technology conference, ‘mobile technology for the human race’, pp. 1835–1838.

  9. Demestichas, P., Georgantas, N., Tzifa, E., Demesticha, V., Striki, M., Kilanioti, M., et al. (2000). Computationally efficient algorithms for location area planning in future cellular systems. Computer Communications, 23, 1263–1280.

    Article  Google Scholar 

  10. Taheri, J., & Zomaya, Y. A. (2005). A genetic algorithm for finding optimal location area configurations for mobility management. In Proceedings of the IEEE conference on local computer networks 30th anniversary, pp. 568–577.

  11. Taheri, J., & Zomaya, Y. A. (2005). A simulated annealing approach for mobile location management. In Proceedings of the 19th IEEE international parallel and distributed processing symposium, pp. 194–201.

  12. Taheri, J., & Zomaya, Y. A. (2006). Clustering techniques for dynamic mobility management. In Proceedings of the 4th ACM international workshop on mobility management and wireless access, pp. 10–17.

  13. Taheri, J., & Zomaya, Y. A. (2004). The use of a Hopfield neural network in solving the mobility management problem. In Proceedings of the IEEE/ACS international conference of pervasive services, pp. 141–150.

  14. Almeida-Luz, S. M., Vega-Rodríguez, M. A., Gómez-Pulido, J. A., & Sánchez-Pérez, J. M. (2011). Differential evolution for solving the mobile location management. Applied Soft Computing, 11, 410–427.

    Article  Google Scholar 

  15. Berrocal-Plaza, V., Vega-Rodríguez, M. A., Gómez-Pulido, J. A., & Sánchez-Pérez, J. M. (2012). A multi-objective approach to solve the location areas problem. In 1st International conference on the theory and practice of natural computing, pp. 72–83.

  16. Berrocal-Plaza, V., Vega-Rodríguez, M. A., Gómez-Pulido, J. A., & Sánchez-Pérez, J. M. (2012). Solving the location areas problem with strength pareto evolutionary algorithm. In Proceedings of the 13th IEEE international symposium on computational intelligence and informatics, pp. 49–54.

  17. Berrocal-Plaza, V., Vega-Rodríguez, M. A., Gómez-Pulido, J. A., & Sánchez-Pérez, J. M. (2013). Solving the location areas scheme in realistic networks by using a multi-objective algorithm. In Proceedings of the 16th European conference on applications of evolutionary computation, pp. 72–81.

  18. Hu, L.-R., & Rappaport, S. S. (1997). An adaptive location management scheme for global personal communications. In Proceedings of IEEE international conference on personal communications, pp. 54–60.

  19. Taheri, J., Zomaya, A. Y., & Iftikhar, M. (2011). Fuzzy online location management in mobile computing environments. Journal of Parallel and Distributed Computing, 71(8), 1142–1153.

    Article  Google Scholar 

  20. Subrata, R., & Zomaya, A. Y. (2003). Evolving cellular automata for location management in mobile computing networks. IEEE Transactions on Parallel and Distributed Systems, 14(1), 13–26.

    Article  Google Scholar 

  21. Zhao, W., & Xie, J. (2011). ReLoAD: resilient location area design for internet-based infrastructure wireless mesh networks. GLOBECOM, pp. 1–5.

  22. Zhao, W., & Xie, J. (2013). DoMaIN: A novel dynamic location management solution for internet-based infrastructure wireless mesh networks. IEEE Transactions on Parallel and Distributed Systems, 24(8), 1514–1524.

    Article  Google Scholar 

  23. Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: Methods and applications. PhD thesis, ETH Zurich.

  24. Knowles, J., Thiele, L., & Zitzler, E. (2006). A tutorial on the performance assessment of stochastic multiobjective optimizers. Computer engineering and networks laboratory (TIK), TIK Report 214, ETH Zurich.

  25. Deb, K. (2001). MultiObjective optimization using evolutionary algorithms (1st ed.). Chichester: Wiley.

    Google Scholar 

  26. Coello, C. A., Lamont, G. L., & Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (2nd ed.). Berlin: Springer.

    MATH  Google Scholar 

  27. Janssens, G. K., & Pangilinan, J. M. (2010). Multiple criteria performance analysis of non-dominated sets obtained by multi-objective evolutionary algorithms for optimization. AIAI, pp. 94–103.

  28. Weise, T. (2009). Global optimization algorithms: Theory and application [Online]. http://www.it-weise.de/.

  29. Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.

    Article  Google Scholar 

  30. Srinivas, N., & Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2, 221–248.

    Article  Google Scholar 

  31. Zitzler, E., Laumanns, M., & Thiele, L. (2002). SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. Evolutionary methods for design, optimisation and control with application to industrial problems, pp. 95–100.

  32. Zitzler, E., & Thiele, L. (1998). An evolutionary algorithm for multiobjective optimization: The strength pareto approach. Computer engineering and communication networks lab (TIK), Tech. Rep. 43, ETH Zürich.

  33. Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical procedures (5th ed.). New York: Chapman & Hall/CRC Press.

    MATH  Google Scholar 

  34. Stanford University Mobile Activity TRAces (SUMATRA). Accessed in 2014 http://infolab.stanford.edu/sumatra.

Download references

Acknowledgments

This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the Contract TIN2012-30685 (BIO project). Víctor Berrocal-Plaza is supported by the research Grant FPU-AP2010-5841 from the Spanish Goverment (Ministerio de Educación).

Author information

Authors and Affiliations

  1. Department of Technologies of Computers and Communications, University of Extremadura Escuela Politécnica, Campus Universitario S/N, 10003, Cáceres, Spain

    Víctor Berrocal-Plaza, Miguel A. Vega-Rodríguez & Juan M. Sánchez-Pérez

Authors
  1. Víctor Berrocal-Plaza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel A. Vega-Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan M. Sánchez-Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Víctor Berrocal-Plaza.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Berrocal-Plaza, V., Vega-Rodríguez, M.A. & Sánchez-Pérez, J.M. Solving the location areas management problem with multi-objective evolutionary strategies. Wireless Netw 20, 1909–1924 (2014). https://doi.org/10.1007/s11276-014-0718-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-014-0718-x

Keywords

Search

Navigation