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Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficie...

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Autores principales: Almalki, Faris A., Soufiene, Ben Othman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915048/
https://www.ncbi.nlm.nih.gov/pubmed/35270932
http://dx.doi.org/10.3390/s22051786
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author Almalki, Faris A.
Soufiene, Ben Othman
author_facet Almalki, Faris A.
Soufiene, Ben Othman
author_sort Almalki, Faris A.
collection PubMed
description The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor.
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spelling pubmed-89150482022-03-12 Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone Almalki, Faris A. Soufiene, Ben Othman Sensors (Basel) Article The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor. MDPI 2022-02-24 /pmc/articles/PMC8915048/ /pubmed/35270932 http://dx.doi.org/10.3390/s22051786 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Almalki, Faris A.
Soufiene, Ben Othman
Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone
title Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone
title_full Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone
title_fullStr Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone
title_full_unstemmed Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone
title_short Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone
title_sort modifying hata-davidson propagation model for remote sensing in complex environments using a multifactional drone
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915048/
https://www.ncbi.nlm.nih.gov/pubmed/35270932
http://dx.doi.org/10.3390/s22051786
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