Abstract
Disasters like floods, avalanches, earthquakes are one of the main causes of death in human history. Search and rescue operations use drones and wireless communication techniques to scan and find the location of victims under rubble. Renowned for its resilience to the different causes of signal attenuation, LoRa wireless communication has been considered as the best candidate to employ for this type of operations. Thus, in this paper, we present a solution based on LoRa radio parameters and Artificial Neural Networks to estimate the distance between the rescue drone and the victim. By using real measurements that represent an actual search and rescue operation, we have achieved distance estimations (between 0 to 120 m) with less than 5% mean error. Add to this, our results, which are based on various LoRa radio parameters, show an improvement of 78% over the mechanisms that use RSSI as the only parameter.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ayele, E., Hakkenberg, C., Meijers, J.P., Zhang, K., Meratnia, N., Havinga, P.M.: Performance analysis of LoRa radio for an indoor IoT applications. In: International Conference on Internet of Things for the Global Community (IoTGC) (2017)
Daramouskas, I., Kapoulas, V., Paraskevas, M.: Using neural networks for RSSI location estimation in LoRa networks. In: IISA (2019)
Ingabire, W., Larijani, H., Gibson, R., Qureshi, A.: Outdoor node localization using random neural networks for large-scale urban IoT LoRa networks. In: MDPI Algorithms, vol. 14, no. 11 (2021)
AbiNehme, J., Nicolas, C., Habib, G., Haddad, N., Duran-Faundez, C.: Experimental study of LoRa performance: a concrete building case. In: 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA) (2021)
Nguyen, T.: LoRa Localisation in Cities with Neural Networks (2019)
Delafontaine, V., Schiano, F., Cocco, G., Rusu, A., Floreano, D.: Drone-aided localization in LoRa IoT networks. In: IEEE International Conference on Robotics and Automation (ICRA) (2020)
Bianco, G.Ma., Giuliano, R., Marrocco, G., Mazzenga, F., Mejia-Aguilar, A.: LoRa system for search and rescue: path loss models and procedures in mountain scenarios. In: IEEE Internet of Things Journal (2021)
Laoudias, C., Moreira, A., Kim, S., Lee, S., Wirola, L., Fischione, C.: A survey of enabling technologies for network localization, tracking, and navigation. In: IEEE Communications Surveys & Tutorials (2018)
Haiahem, R., Minet, P., Boumerdassi, S., Azouz Saidane, L.: Collision-free transmissions in an IoT monitoring application based on LoRaWAN. In: Sensors, MDPI, 2020, ff10.3390/s20144053ff. ffhal-02908985
Acknowledgements
This work was supported by the Lebanese University under Grant 2019 and partially funded by the ANR INTELLIGENTSIA (nb. ANR-20-CE25-0011).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Abboud, M., Nicolas, C., Habib, G. (2022). Distance Estimation Using LoRa and Neural Networks. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2021. Lecture Notes in Computer Science, vol 13175. Springer, Cham. https://doi.org/10.1007/978-3-030-98978-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-98978-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98977-4
Online ISBN: 978-3-030-98978-1
eBook Packages: Computer ScienceComputer Science (R0)