Node Location Optimization for Localizing UAVs in Urban Scenarios

  1. Verde, Paula
  2. Ferrero-Guillén, Rubén
  3. Alija-Pérez, José-Manuel
  4. Martínez-Gutiérrez, Alberto
  5. Díez-González, Javier
  6. Perez, Hilde
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Libro:
Lecture Notes in Networks and Systems

ISSN: 2367-3370 2367-3389

ISBN: 9783031180491 9783031180507

Año de publicación: 2022

Páginas: 616-625

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-031-18050-7_60 GOOGLE SCHOLAR lock_openAcceso abierto editor

Objetivos de desarrollo sostenible

Referencias bibliográficas

  • Gupta, S.G., Ghonge, D., Jawandhiya, P.M., et al.: Review of unmanned aircraft system (UAS). Int. J. Adv. Res. Comput. Eng. Technol. 2 (2013)
  • Semsch, E., Jakob, M., Pavlicek, D., Pechoucek, M.: Autonomous UAV surveillance in complex urban environments. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 2, pp. 82–85. IEEE (2009)
  • Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., Siegwart, R.: A UAV system for inspection of industrial facilities. In: 2013 IEEE Aerospace Conference, pp. 1–8. IEEE (2013)
  • Chiang, W.-C., Li, Y., Shang, J., Urban, T.L.: Impact of drone delivery on sustainability and cost: realizing the UAV potential through vehicle routing optimization. Appl. Energy 242, 1164–1175 (2019)
  • Fernández-Caramés, T.M., Blanco-Novoa, O., Froiz-Míguez, I., Fraga-Lamas, P.: Towards an autonomous industry 4.0 warehouse: a UAV and blockchain-based system for inventory and traceability applications in big data-driven supply chain management. Sensors 19(10), 2394 (2019)
  • Kim, J., Kim, S., Ju, C., Son, H.I.: Unmanned aerial vehicles in agriculture: a review of perspective of platform, control, and applications. IEEE Access 7, 105100–105115 (2019)
  • Brunner, G., Szebedy, B., Tanner, S., Wattenhofer, R.: The urban last mile problem: autonomous drone delivery to your balcony. In: 2019 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1005–1012. IEEE (2019)
  • Pavlenko, T., Schütz, M., Vossiek, M., Walter, T., Montenegro, S.: Wireless local positioning system for controlled UAV landing in GNSS-denied environment. In: 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 171–175. IEEE (2019)
  • Bijjahalli, S., Gardi, A., Sabatini, R.: GNSS performance modelling for positioning and navigation in urban environments. In: 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 521–526. IEEE (2018)
  • Arifin, M., Nazaruddin, Y., Tamba, T., Santosa, R., Widyotriatmo, A.: Experimental modeling of a quadrotor UAV using an indoor local positioning system. In: 2018 5th International Conference on Electric Vehicular Technology (ICEVT), pp. 25–30. IEEE (2018)
  • Amar, A., Weiss, A.J.: Localization of narrowband radio emitters based on doppler frequency shifts. IEEE Trans. Signal Process. 56(11), 5500–5508 (2008)
  • Wang, Y., Ma, X., Leus, G.: Robust time-based localization for asynchronous networks. IEEE Trans. Signal Process. 59(9), 4397–4410 (2011)
  • Zhao, W., Panerati, J., Schoellig, A.P.: Learning-based bias correction for time difference of arrival ultra-wideband localization of resource-constrained mobile robots. IEEE Robot. Automat. Lett. 6(2), 3639–3646 (2021)
  • Deng, Z., Tang, S., Deng, X., Yin, L., Liu, J.: A novel location source optimization algorithm for low anchor node density wireless sensor networks. Sensors 21(5), 1890 (2021)
  • Khalaf-Allah, M.: Novel solutions to the three-anchor TOA-based three-dimensional positioning problem. Sensors 21(21), 7325 (2021)
  • 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)
  • Hu, J., et al.: A brief review on the positioning technologies for unmanned aerial vehicles. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), pp. 527–532. IEEE (2017)
  • 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)
  • Díez-González, J., et al.: Genetic algorithm approach to the 3d node localization in TDOA systems. Sensors 19(18), 3880 (2019)
  • Rajakumar, R., Amudhavel, J., Dhavachelvan, P., Vengattaraman, T.: GWO-LPWSN: grey wolf optimization algorithm for node localization problem in wireless sensor networks. J. Comput. Netw. Commun. 2017 (2017)
  • Maheshwari, P., Sharma, A.K., Verma, K.: Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw. 110, 102317 (2021)
  • Kannan, A.A., Mao, G., Vucetic, B.: Simulated annealing based wireless sensor network localization. J. Comput. 1(2), 15–22 (2006)
  • Annepu, V., Rajesh, A.: Implementation of self adaptive mutation factor and cross-over probability based differential evolution algorithm for node localization in wireless sensor networks. Evolution. Intell. 12(3), 469–478 (2019)
  • Bajovic, D., Sinopoli, B., Xavier, J.: Sensor selection for event detection in wireless sensor networks. IEEE Trans. Signal Process. 59(10), 4938–4953 (2011)
  • Vankayalapati, N., Kay, S., Ding, Q.: TDOA based direct positioning maximum likelihood estimator and the Cramer-Rao bound. IEEE Trans. Aerosp. Electron. Syst. 50(3), 1616–1635 (2014)
  • 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)
  • Yang, B., Scheuing, J.: Cramer-Rao bound and optimum sensor array for source localization from time differences of arrival. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), vol. 4, pp. iv–961. IEEE (2005)
  • 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)
  • Díez-González, J., et al.: Optimal node distribution in wireless sensor networks considering sensor selection. In: Sanjurjo González, H., et al. (eds.) SOCO 2021. AISC, vol. 1401, pp. 512–522. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-87869-6_49
  • Mirjalili, S.: Genetic algorithm. In: Evolutionary Algorithms and Neural Networks. SCI, vol. 780, pp. 43–55. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93025-1_4
  • 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
  • Wang, L., Groves, P.D., Ziebart, M.K.: GNSS shadow matching: improving urban positioning accuracy using a 3D city model with optimized visibility scoring scheme. NAVIGATION, J. Inst. Navigat. 60(3), 195–207 (2013)
  • Bouloukou, M., Masiero, A., Vettore, A., Gikas, V.: UAV UWB positioning close to building facades: a case study. Int. Archiv. Photogram. Remote Sens. Spatial Inf. Sci. 43, 97–102 (2021)