LoRa rescue: Emergency distress signal geolocation system

Clicks: 4
ID: 285997
2021
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #326 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
persistent_1760657270_68f17f76adfad Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Uy, Benjamin Emmanuel C.
Journal Malay Journal
Year 2021
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdb_ece/6
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.