Skip to main content
SpringerLink
Log in

Black Widow Optimization for the Node Location Problem in Localization Wireless Sensor Networks

  • Conference paper
  • First Online:
Hybrid Artificial Intelligent Systems (HAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13469))

Included in the following conference series:

  • 695 Accesses

Abstract

Local Positioning Systems (LPS) present higher performance and more accurate target localization than traditional GNSS systems in harsh environments. However, the Node Location Problem (NLP) stands as one of the most important problems when designing LPS since the achievement of optimized sensor distributions in space requires addressing this NP-Hard problem. Therefore, it is common the employment of metaheuristics to tackle this problem. In this sense, the fundamentals of the no free lunch theorems state that, in order to obtain improved results for a specific problem, an investigation on the heuristic that best suits for the characteristics of the problem must be considered. Therefore, in this paper, we propose the application of the black widow optimization algorithm for the first time in the literature for the NLP. This metaheuristic allows a more diversified search adapting to the discontinuous landscape fitness of the NLP when considering NLOS links among the positioning signals. The results obtained are compared with those by a canonical genetic algorithm (CGA) introduced in our previous research, outperforming the localization error by 15% and 10% the single-point and multipoint crossover CGAs analyzed.

This research has been funded by the Spanish Research Agency (AEI) grant number PID2019-108277GB-C21/AEI/10.13039/501100011033.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Al-Qaisi, A., Alhasanat, A.I., Mesleh, A., Sharif, B.S., Tsimenidis, C.C., Neasham, J.A.: Quantized lower bounds on grid-based localization algorithm for wireless sensor networks. Ann. Telecommun. 239–249 (2016). https://doi.org/10.1007/s12243-016-0494-y

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Alvarez, R., Diez-Gonzalez, J., Strisciuglio, N., Perez, H.: Multi-objective optimization for asynchronous positioning systems based on a complete characterization of ranging errors in 3D complex environments. IEEE Access 8, 43046–43056 (2020)

    Article  Google Scholar 

  5. Álvarez, R., Díez-González, J., Verde, P., Perez, H.: Comparative performance analysis of time local positioning architectures in NLOS urban scenarios. IEEE Access 8, 225258–225271 (2020)

    Article  Google Scholar 

  6. Diamant, R., Lampe, L.: Underwater localization with time-synchronization and propagation speed uncertainties. IEEE Trans. Mob. Comput. 12(7), 1257–1269 (2012)

    Article  Google Scholar 

  7. 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 

  8. Díez-Gonzalez, J., Álvarez, R., Perez, H.: Optimized cost-effective node deployments in asynchronous time local positioning systems. IEEE Access 8, 154671–154682 (2020)

    Article  Google Scholar 

  9. Diez-Gonzalez, J., Alvarez, R., Prieto-Fernandez, N., Perez, H.: Local wireless sensor networks positioning reliability under sensor failure. Sensors 20(5), 1426 (2020)

    Article  Google Scholar 

  10. 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 

  11. Díez-González, J., Álvarez, R., Verde, P., Ferrero-Guillén, R., Perez, H.: Analysis of reliable deployment of TDOA local positioning architectures. Neurocomputing 484, 149–160 (2022)

    Article  Google Scholar 

  12. Díez-González, J., Verde, P., Ferrero-Guillén, R., Álvarez, R., Pérez, H.: Hybrid memetic algorithm for the node location problem in local positioning systems. Sensors 20(19), 5475 (2020)

    Article  Google Scholar 

  13. 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 

  14. Ferrero-Guillén, R., Díez-González, J., Álvarez, R., Pérez, H.: 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.) HAIS 2020. LNCS (LNAI), vol. 12344, pp. 273–283. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61705-9_23

    Chapter  Google Scholar 

  15. Gabriel, P.H.R., Delbem, A.C.B.: Fundamentos de algoritmos evolutivos (2008)

    Google Scholar 

  16. Gupta, P., Tripathi, S., Singh, S.: RDA-BWO: hybrid energy efficient data transfer and mobile sink location prediction in heterogeneous WSN. Wirel. Netw. 27(7), 4421–4440 (2021)

    Article  Google Scholar 

  17. Gupta, S.K., Kuila, P., Jana, P.K.: Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput. Electr. Eng. 56, 544–556 (2016)

    Article  Google Scholar 

  18. Guvenc, I., Chong, C.C.: A survey on TOA based wireless localization and NLOS mitigation techniques. IEEE Commun. Surv. Tutor. 11(3), 107–124 (2009)

    Article  Google Scholar 

  19. Hayyolalam, V., Kazem, A.A.P.: Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2020)

    Google Scholar 

  20. Igel, C.: No free lunch theorems: limitations and perspectives of metaheuristics. In: Borenstein, Y., Moraglio, A. (eds.) Theory and Principled Methods for the Design of Metaheuristics. NCS, pp. 1–23. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-33206-7_1

    Chapter  MATH  Google Scholar 

  21. Kaune, R., Hörst, J., Koch, W.: Accuracy analysis for TDOA localization in sensor networks. In: 14th International Conference on Information Fusion, pp. 1–8. IEEE (2011)

    Google Scholar 

  22. Kolodziej, K.W., Hjelm, J.: Local Positioning Systems: LBS Applications and Services. CRC Press (2017)

    Google Scholar 

  23. Li, Q., Chen, B., Yang, M.: Time difference of arrival passive localization sensor selection method based on Tabu search. Sensors 20(22), 6547 (2020)

    Article  Google Scholar 

  24. Mirjalili, S.: Evolutionary Algorithms and Neural Networks. Studies in Computational Intelligence, vol. 780. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-319-93025-1

    Book  MATH  Google Scholar 

  25. Nguyen, N.T., Liu, B.H.: The mobile sensor deployment problem and the target coverage problem in mobile wireless sensor networks are NP-hard. IEEE Syst. J. 13(2), 1312–1315 (2018)

    Article  Google Scholar 

  26. Salles, L.A., Vani, B.C., Moraes, A., Costa, E., de Paula, E.R.: Investigating ionospheric scintillation effects on multifrequency GPS signals. Surv. Geophys. 42(4), 999–1025 (2021)

    Article  Google Scholar 

  27. Seow, C.K., Tan, S.Y.: Non-line-of-sight localization in multipath environments. IEEE Trans. Mob. Comput. 7(5), 647–660 (2008)

    Article  Google Scholar 

  28. Sheriba, S., Rajesh, D.H.: Energy-efficient clustering protocol for WSN based on improved black widow optimization and fuzzy logic. Telecommun. Syst. 77(1), 213–230 (2021)

    Article  Google Scholar 

  29. Skog, I., Handel, P.: Time synchronization errors in loosely coupled GPS-aided inertial navigation systems. IEEE Trans. Intell. Transp. Syst. 12(4), 1014–1023 (2011)

    Article  Google Scholar 

  30. Strumberger, I., Minovic, M., Tuba, M., Bacanin, N.: Performance of elephant herding optimization and tree growth algorithm adapted for node localization in wireless sensor networks. Sensors 19(11), 2515 (2019)

    Article  Google Scholar 

  31. Umbarkar, A.J., Sheth, P.D.: Crossover operators in genetic algorithms: a review. ICTACT J. Soft Comput. 6(1) (2015)

    Google Scholar 

  32. Vecchio, M., López-Valcarce, R., Marcelloni, F.: A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks. Appl. Soft Comput. 12(7), 1891–1901 (2012)

    Article  Google Scholar 

  33. Verde, P., Ferrero-Guillén, R., Álvarez, R., Díez-González, J., Perez, H.: Node distribution optimization in positioning sensor networks through memetic algorithms in urban scenarios. In: Engineering Proceedings, vol. 2, p. 73. Multidisciplinary Digital Publishing Institute (2020)

    Google Scholar 

  34. Wang, Y., Ho, K.: TDOA source localization in the presence of synchronization clock bias and sensor position errors. IEEE Trans. Signal Process. 61(18), 4532–4544 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Paula Verde, Javier Díez-González, Alberto Martínez-Gutiérrez, Rubén Ferrero-Guillén, Rubén Álvarez & Hilde Perez

Authors
  1. Paula Verde
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javier Díez-González
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alberto Martínez-Gutiérrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rubén Ferrero-Guillén
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rubén Álvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hilde Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paula Verde .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Analytics Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Applied Computational Intelligence, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  8. Universidad Pablo de Olavide, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Verde, P., Díez-González, J., Martínez-Gutiérrez, A., Ferrero-Guillén, R., Álvarez, R., Perez, H. (2022). Black Widow Optimization for the Node Location Problem in Localization Wireless Sensor Networks. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15471-3_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15470-6

  • Online ISBN: 978-3-031-15471-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation