relation: https://khub.utp.edu.my/scholars/19332/
title: Support Vector Regression based Localization Approach using LoRaWAN
creator: Magsi, S.A.
creator: Khir, M.H.B.M.
creator: Nawi, I.B.M.
creator: Saboor, A.
creator: Siddiqui, M.A.
description: The Internet of Things (IoT) domain has experienced significant growth in recent times. There has been extensive research conducted in various areas of IoT, including localization. Localization of Long Range (LoRa) nodes in outdoor environments is an important task for various applications, including asset tracking and precision agriculture. In this research article, a localization approach using Support Vector Regression (SVR) has been implemented to predict the location of the end node using LoRaWAN. The experiments are conducted in the outdoor campus environment. The SVR used the Received Signal Strength Indicator (RSSI) fingerprints to locate the end nodes. The results show that the proposed method can locate the end node with a minimum error of 36.26 meters and a mean error of 171.59 meters © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.
date: 2023
type: Article
type: PeerReviewed
identifier:   Magsi, S.A. and Khir, M.H.B.M. and Nawi, I.B.M. and Saboor, A. and Siddiqui, M.A.  (2023) Support Vector Regression based Localization Approach using LoRaWAN.  International Journal of Advanced Computer Science and Applications, 14 (3).  pp. 307-312.      
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151803729&doi=10.14569%2fIJACSA.2023.0140335&partnerID=40&md5=004ecf73972313e0b6eb445342a7aba5
relation: 10.14569/IJACSA.2023.0140335
identifier: 10.14569/IJACSA.2023.0140335