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Conference Papers Year : 2022

Distance Estimation Using LoRa and Neural Networks

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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.
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hal-03659859 , version 1 (05-05-2022)

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Mira Abboud, Charbel Nicolas, Gilbert Habib. Distance Estimation Using LoRa and Neural Networks. Machine Learning for Networking, Dec 2021, Paris, France. pp.148-159, ⟨10.1007/978-3-030-98978-1_10⟩. ⟨hal-03659859⟩
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