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Analysis of the Genetic Algorithm Operators for the Node Location Problem in Local Positioning Systems

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

The node location plays a critical role in the LPS performance capabilities. Due to the complexity of this problem, the implementation of heuristic methodologies such as genetic algorithms (GA) has been widely proposed in the literature. However, the performance of GA is heavily dependent of the consistency of its foundation and its adaptation to the nature of the optimization problem. In this paper, we analyze and compare a variety of different selection and crossover techniques in search for the most suitable configuration for the node location problem. Results show that although some combinations achieve adequate results, the concept of a hybrid GA that takes advantage from different configurations depending on the problem requirements can surpass any fixed individual combination.

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References

  1. de Sousa, M.N., Thomä, R.S.: Enhancement of localization systems in NLOS urban scenario with multipath ray tracing fingerprints and machine learning. Sensors 18(11), 4073 (2018)

    Article  Google Scholar 

  2. Groves, P.D., Adjrad, M.: Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges. GPS Solut. 21(4), 1805–1816 (2017). https://doi.org/10.1007/s10291-017-0654-1

    Article  Google Scholar 

  3. Stankov, S.M., Jakowski, N.: Ionospheric effects on GNSS reference network integrity. J. Atmos. Solar-Terr. Phys. 69(4–5), 485–499 (2007)

    Article  Google Scholar 

  4. Álvarez, R., Díez-González, J., Sánchez-González, L., Pérez, H.: Combined noise and clock CRLB error model for the optimization of node location in time positioning systems. IEEE Access 8(1), 31910–31919 (2020)

    Article  Google Scholar 

  5. Tekdas, O., Isler, V.: Sensor placement for triangulation-based localization. IEEE Trans. Autom. Sci. Eng. 7(3), 681–685 (2010)

    Article  Google Scholar 

  6. Yoon, Y., Kim, Y.-H.: An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans. Cybern. 43(5), 1473–1483 (2013)

    Article  Google Scholar 

  7. Laguna, M., Roa, J.O., Jiménez, A.R., Seco, F.: Diversified local search for the optimal layout of beacons in an indoor positioning system. IIE Trans. 41(3), 247–259 (2009)

    Article  Google Scholar 

  8. Tuba, E., Tuba, M., Beko, M.: Two stage wireless sensor node localization using firefly algorithm. In: Yang, X.-S., Nagar, A.K., Joshi, A. (eds.) Smart Trends in Systems, Security and Sustainability. LNNS, vol. 18, pp. 113–120. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6916-1_10

    Chapter  Google Scholar 

  9. Kannadasan, K., Edla, D.R., Kongara, M.C., Kuppili, V.: M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm. Wirel. Netw. 26(4), 2769–2783 (2019). https://doi.org/10.1007/s11276-019-02032-4

    Article  Google Scholar 

  10. Wang, X., Ma, J.J., Wang, S., Bi, D.W.: Distributed particle swarm optimization and simulated annealing for energy-efficient coverage in wireless sensor networks. Sensors 7(5), 628–648 (2007)

    Article  Google Scholar 

  11. Díez-González, J., Álvarez, R., González-Bárcena, D., Sánchez-González, L., Castejón-Limas, M., Perez, H.: Genetic algorithm approach to the 3D node localization in TDOA systems. Sensors 19(18), 3880 (2019)

    Article  Google Scholar 

  12. Domingo-Perez, F., Lazaro-Galilea, J.L., Wieser, A., Martin-Gorostiza, E., Salido-Monzu, D., de la Llana, A.: Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization. Expert Syst. Appl. 47, 95–105 (2016)

    Article  Google Scholar 

  13. Álvarez, R., Díez-González, J., Strisciuglio, N., Pérez, H.: Multi-objective optimization for asynchronous positioning methods. IEEE Access 8(1), 43046–43056 (2020)

    Article  Google Scholar 

  14. Kaune, R., Hörst, J., Koch, W.: Accuracy analysis for TDOA localization in sensor networks. In: 14th International Conference on Information Fusion, Chicago (2011)

    Google Scholar 

  15. Díez-González, J., Álvarez, R., Sánchez-González, L., Fernández-Robles, L., Pérez, H., Castejón-Limas, M.: 3D TDOA problem solution with four receiving nodes. Sensors 19(13), 2892 (2019)

    Article  Google Scholar 

  16. Díez-González, J., Álvarez, R., Prieto-Fernández, N., Pérez, H.: Local wireless sensor networks positioning reliability under sensor failure. Sensors 20(5), 1426 (2020)

    Article  Google Scholar 

  17. Huang, B., Xie, L., Yang, Z.: TDOA-based source localization with distance-dependent noises. IEEE Trans. Wirel. Commun. 14(1), 468–480 (2015)

    Article  Google Scholar 

  18. Álvarez, R., Díez-González, J., Alonso, E., Fernández-Robles, L., Castejón-Limas, M., Pérez, H.: Accuracy analysis in sensor networks for asynchronous positioning methods. Sensors 19(13), 3024 (2019)

    Article  Google Scholar 

  19. Hu, D., Chen, S., Bai, H., Zhao, C., Luo, L.: CRLB for joint estimation of TDOA, phase, FDOA, and Doppler rate. J. Eng. 21, 7628–7631 (2019)

    Google Scholar 

  20. Kowalski, M., Willett, P., Fair, T., Bar-Shalom, Y.: CRLB for estimating time-varying rotational biases in passive sensors. IEEE Trans. Aerosp. Electron. Syst. 56(1), 343–355 (2020)

    Article  Google Scholar 

  21. He, S., Dong, X.: High-accuracy localization platform using asynchronous time difference of arrival technology. IEEE Trans. Instrum. Measur. 66(7), 1728–1742 (2017)

    Article  Google Scholar 

  22. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press (1992)

    Google Scholar 

  23. Goldberg, D.E.: Genetic Algorithms. Pearson Education India (2006)

    Google Scholar 

  24. Razali, N.M., Geraghty, J.: Genetic algorithm performance with different selection strategies in solving TSP. In: Proceedings of the World Congress on Engineering, vol. 2, no. 1. International Association of Engineers, Hong Kong (2011)

    Google Scholar 

  25. De Jong, K.A., Spears, W.M.: A formal analysis of the role of multi-point crossover in genetic algorithms. Ann. Math. Artif. Intell. 5(1), 1–26 (1992)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical, Computer, and Aerospace Engineering, Universidad de León, 24071, León, Spain

    Rubén Ferrero-Guillén, Javier Díez-González & Hilde Pérez

  2. Positioning Department, Drotium, Universidad de León, 24071, León, Spain

    Rubén Álvarez

Authors
  1. Rubén Ferrero-Guillén
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  2. Javier Díez-González
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  3. Rubén Álvarez
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  4. Hilde Pérez
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Corresponding authors

Correspondence to Rubén Ferrero-Guillén , Javier Díez-González , Rubén Álvarez or Hilde Pérez .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Ferrero-Guillén, R., Díez-González, J., Álvarez, R., Pérez, H. (2020). Analysis of the Genetic Algorithm Operators for the Node Location Problem in Local Positioning Systems. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_23

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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