%0 Journal Article
%A Magsi, S.A.
%A Khir, M.H.B.M.
%A Nawi, I.B.M.
%A Saboor, A.
%A Siddiqui, M.A.
%D 2023
%F scholars:19332
%J International Journal of Advanced Computer Science and Applications
%K Support vector regression; Vectors, Asset tracking; Fingerprinting; Localisation; LoRaWAN; Mean errors; Outdoor environment; Precision Agriculture; Received signal strength indicators; Support vector regressions, Internet of things
%N 3
%P 307-312
%R 10.14569/IJACSA.2023.0140335
%T Support Vector Regression based Localization Approach using LoRaWAN
%U https://khub.utp.edu.my/scholars/19332/
%V 14
%X 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.
%Z cited By 0