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Design of deer hunting optimization algorithm for accurate 3D indoor node localization

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Abstract

Indoor localization is one of the emergent technologies in location based services, which find useful for commercial as well as civilian industries. Global position systems are a familiar solution for outdoor localization systems. But the presence of complicated obstacles in buildings poses a major challenge in indoor localization. Though few indoor localization techniques based on ranging and fingerprint based techniques are devised, they are time consuming and laborious. Therefore, this paper devises an efficient deer hunting optimization algorithm with weighted least square estimation (DHOA-WLSE) technique for accurate 3D indoor node localization technique. The proposed DHOA-WLSE technique has the ability to accomplish minimal localization error with least localization time. In DHOA-WLSE technique, the DHOA is used to estimate the primary target location to eliminate the non-line of sight errors. Based on the primary locations attained, the WLSE technique is applied to determine the accurate target’s final location. In order to validate the 3D indoor localization performance of the DHOA-WLSE technique, an extensive simulation analysis is performed and the results are investigated in terms of different measures. The simulation outcomes demonstrated the superior performance of the DHOA-WLSE technique over the recent state of art techniques.

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References

  1. Yi P, Yu M, Zhou Z, Xu W, Zhang Q, Zhu T (2014) A three-dimensional wireless indoor localization system. J Electr Comput Eng

  2. Chen S, Shi Z, Wu F, Wang C, Liu J, Chen J (2018) Improved 3-D indoor positioning based on particle swarm optimization and the Chan method. Information 9(9):208

    Article  Google Scholar 

  3. Wang X, Wang Z, O’Dea B (2003) A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation. IEEE Trans Veh Technol 52(1):112–116

    Article  Google Scholar 

  4. Wang P, He J, Xu L, Wu Y, Xu C, Zhang X (2017) Characteristic modeling of TOA ranging error in rotating anchor-based relative positioning. IEEE Sens J 17:7945–7953

    Article  Google Scholar 

  5. Wylie MP, Holtzman J (1996) The non-line of sight problem in mobile location estimation. In: Proceedings of the 5th international conference on universal personal communications, Cambridge, MA, USA, pp 827–831

  6. Luo J, Shukla HV, Hubaux JP (2006) Non-interactive location surveying for sensor networks with mobility-differentiated TOA. In: Proceedings of the 25th IEEE INFOCOM, Barcelona, Spain, 23–29 April 2006, pp 1–12

  7. Chang X, Ye S, Jiang Y, Guan T, Wang J (2017) Three-dimensional positioning of wireless communication base station. In: Proceedings of the 2017 IEEE 2nd advanced information technology, electronic and automation control conference, Chongqing, China, 25–26 March 2017

  8. Chen PC (1999) A non-line-of-sight error mitigation algorithm in location estimation. In: Proceedings of the 1999 IEEE wireless communications and networking conference (Cat. No. 99TH8466), New Orleans, LA, USA, 21–24 September 1999, pp 316–320

  9. Abolfathi A, Behnia F, Marvasti F (2018) NLOS mitigation using sparsity feature and iterative methods. arXiv: http://arxiv.org/abs/1803.06838

  10. Miura S, Kamijo S (2015) GPS error correction by multipath adaptation. Int J Intell Transp Syst Res 13:1–8

    Google Scholar 

  11. Al-Jazzar S, Caffery J, You HR (2002) A scattering model based approach to NLOS mitigation in TOA location systems. In: Proceedings of the IEEE 55th vehicular technology conference, VTC Spring 2002 (Cat. No.02CH37367), Birmingham, AL, USA, 6–9 August 2002, pp 861–865

  12. Cheng S, Wang S, Guan W, Xu H, Li P (2020) 3DLRA: an RFID 3D indoor localization method based on deep learning. Sensors 20(9):2731

    Article  Google Scholar 

  13. Cruz O, Ramos E, Ramírez M (2011) 3D indoor location and navigation system based on Bluetooth. In: CONIELECOMP 2011, 21st International conference on electrical communications and computers. IEEE, pp 271–277

  14. Yu Y, Chen R, Chen L, Xu S, Li W, Wu Y, Zhou H (2020) Precise 3-D indoor localization based on Wi-Fi FTM and built-in sensors. IEEE Internet Things J 7(12):11753–11765

    Article  Google Scholar 

  15. Jaworski W, Wilk P, Zborowski P, Chmielowiec W, Lee AY, Kumar A (2017) Real-time 3d indoor localization. In: 2017 International conference on indoor positioning and indoor navigation (IPIN), pp. 1–8

  16. El Boudani B, Kanaris L, Kokkinis A, Kyriacou M, Chrysoulas C, Stavrou S, Dagiuklas T (2020) Implementing deep learning techniques in 5G IoT networks for 3D indoor positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture). Sensors 20(19):5495

    Article  Google Scholar 

  17. Li J, Wang C, Kang X, Zhao Q (2019) Camera localization for augmented reality and indoor positioning: a vision-based 3D feature database approach. Int J Digit Earth

  18. Zhao T, Own CM, Xu C (2016) 3d positioning information on augmented identification for indoor localization. In: Proceedings of the 2nd international conference on communication and information processing, pp 216–221

  19. Paredes JA, Álvarez FJ, Aguilera T, Villadangos JM (2018) 3D indoor positioning of UAVs with spread spectrum ultrasound and time-of-flight cameras. Sensors 18(1):89

    Google Scholar 

  20. Zhang L, Li X, Wei F, Li Y (2020) Indoor 3-D localization based on simulated annealing bat algorithm. Int J Electr Eng Educ 0020720920931067.

  21. Bernardini F, Buffi A, Fontanelli D, Macii D, Magnago V, Marracci M, Motroni A, Nepa P, Tellini B (2020) Robot-based indoor positioning of UHF-RFID tags: The SAR method with multiple trajectories. IEEE Trans Instrum Meas 70:1–15

    Article  Google Scholar 

  22. Shi Q, Huo H, Fang T, Li D (2009) A 3D node localization scheme for wireless sensor networks. IEICE Electronics express 6(3):167–172

    Article  Google Scholar 

  23. Brammya G, Praveena S, Ninu Preetha NS, Ramya R, Rajakumar BR, Binu D (2019) Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput J

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Correspondence to P. Durgaprasadarao.

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Durgaprasadarao, P., Siddaiah, N. Design of deer hunting optimization algorithm for accurate 3D indoor node localization. Evol. Intel. 16, 509–518 (2023). https://doi.org/10.1007/s12065-021-00673-z

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  • DOI: https://doi.org/10.1007/s12065-021-00673-z

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