LoRa rescue: Emergency distress signal geolocation system

In a calamity, cellular networks, internet services, and electricity may prove to be unreliable. And yet, emergency responders will need a way to contact and locate people-in-need as soon as possible. As such, this thesis proposes an emergency trilateration system that uses LoRa. A wireless communic...

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Main Authors: Pe, Gregory James G., Quijano, Robert Ianny Roi F., Uy, Benjamin Emmanuel C.
Format: text
Language:English
Published: Animo Repository 2021
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Online Access:https://animorepository.dlsu.edu.ph/etdb_ece/6
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1006&context=etdb_ece
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etdb_ece-10062022-02-09T01:05:45Z LoRa rescue: Emergency distress signal geolocation system Pe, Gregory James G. Quijano, Robert Ianny Roi F. Uy, Benjamin Emmanuel C. In a calamity, cellular networks, internet services, and electricity may prove to be unreliable. And yet, emergency responders will need a way to contact and locate people-in-need as soon as possible. As such, this thesis proposes an emergency trilateration system that uses LoRa. A wireless communications technology characterized by its longer range and lower power consumption compared to other technologies such as Bluetooth and WiFi. This thesis developed a LoRa trilateration algorithm that improves upon the standard trilateration algorithm by using the closest points. This improved trilateration algorithm obtained a 73.0185% improvement in accuracy compared to the standard algorithm, across 25 datasets. Furthermore, the thesis utilized automated elbow methods to acquire the optimal parameters required by the DBSCAN and K-means clustering algorithms. For DBSCAN, the study automated the acquisition of the Epsilon and MinPts parameters, and for K-means, the SoSD parameter. After applying these clustering algorithms to filter the data, the results provided an average error of 39.6506 meters from the actual position. 2021-12-01T08:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etdb_ece/6 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1006&context=etdb_ece Electronics And Communications Engineering Bachelor's Theses English Animo Repository Wireless communication systems Emergency management Satellite interference geolocation technology Electrical and Computer Engineering Electrical and Electronics Systems and Communications
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Wireless communication systems
Emergency management
Satellite interference geolocation technology
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
spellingShingle Wireless communication systems
Emergency management
Satellite interference geolocation technology
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
Pe, Gregory James G.
Quijano, Robert Ianny Roi F.
Uy, Benjamin Emmanuel C.
LoRa rescue: Emergency distress signal geolocation system
description In a calamity, cellular networks, internet services, and electricity may prove to be unreliable. And yet, emergency responders will need a way to contact and locate people-in-need as soon as possible. As such, this thesis proposes an emergency trilateration system that uses LoRa. A wireless communications technology characterized by its longer range and lower power consumption compared to other technologies such as Bluetooth and WiFi. This thesis developed a LoRa trilateration algorithm that improves upon the standard trilateration algorithm by using the closest points. This improved trilateration algorithm obtained a 73.0185% improvement in accuracy compared to the standard algorithm, across 25 datasets. Furthermore, the thesis utilized automated elbow methods to acquire the optimal parameters required by the DBSCAN and K-means clustering algorithms. For DBSCAN, the study automated the acquisition of the Epsilon and MinPts parameters, and for K-means, the SoSD parameter. After applying these clustering algorithms to filter the data, the results provided an average error of 39.6506 meters from the actual position.
format text
author Pe, Gregory James G.
Quijano, Robert Ianny Roi F.
Uy, Benjamin Emmanuel C.
author_facet Pe, Gregory James G.
Quijano, Robert Ianny Roi F.
Uy, Benjamin Emmanuel C.
author_sort Pe, Gregory James G.
title LoRa rescue: Emergency distress signal geolocation system
title_short LoRa rescue: Emergency distress signal geolocation system
title_full LoRa rescue: Emergency distress signal geolocation system
title_fullStr LoRa rescue: Emergency distress signal geolocation system
title_full_unstemmed LoRa rescue: Emergency distress signal geolocation system
title_sort lora rescue: emergency distress signal geolocation system
publisher Animo Repository
publishDate 2021
url https://animorepository.dlsu.edu.ph/etdb_ece/6
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1006&context=etdb_ece
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