WiFi-based indoor localization
In recent years, the demands for a better system that enables indoor localization have risen exponentially due to the increased pervasiveness of location-based services in various applications within our daily lives. While existing positioning technology such as the Global Positioning System (GPS) w...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/156599 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-156599 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1565992022-04-21T02:00:38Z WiFi-based indoor localization Wong, Shi Heng Sinno Jialin Pan School of Computer Science and Engineering sinnopan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence In recent years, the demands for a better system that enables indoor localization have risen exponentially due to the increased pervasiveness of location-based services in various applications within our daily lives. While existing positioning technology such as the Global Positioning System (GPS) works sufficiently well in outdoor environments, the absence of GPS signals in indoor environments meant that it is not a feasible solution. Hence, this has pushed the interest of creating new and more robust indoor positioning systems (IPS) to greater heights. With that in mind, this study aims to provide a comparison in the performance of several IPS implementations through the use of Wi-Fi technology as well as advanced machine learning techniques. Through our experiments, we show that developing Wi-Fi-based indoor localization systems using machine learning is a viable and high-performing method. Bachelor of Engineering (Computer Engineering) 2022-04-21T02:00:37Z 2022-04-21T02:00:37Z 2022 Final Year Project (FYP) Wong, S. H. (2022). WiFi-based indoor localization. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156599 https://hdl.handle.net/10356/156599 en SCSE21-040 application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
spellingShingle |
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Wong, Shi Heng WiFi-based indoor localization |
description |
In recent years, the demands for a better system that enables indoor localization have risen exponentially due to the increased pervasiveness of location-based services in various applications within our daily lives. While existing positioning technology such as the Global Positioning System (GPS) works sufficiently well in outdoor environments, the absence of GPS signals in indoor environments meant that it is not a feasible solution. Hence, this has pushed the interest of creating new and more robust indoor positioning systems (IPS) to greater heights. With that in mind, this study aims to provide a comparison in the performance of several IPS implementations through the use of Wi-Fi technology as well as advanced machine learning techniques. Through our experiments, we show that developing Wi-Fi-based indoor localization systems using machine learning is a viable and high-performing method. |
author2 |
Sinno Jialin Pan |
author_facet |
Sinno Jialin Pan Wong, Shi Heng |
format |
Final Year Project |
author |
Wong, Shi Heng |
author_sort |
Wong, Shi Heng |
title |
WiFi-based indoor localization |
title_short |
WiFi-based indoor localization |
title_full |
WiFi-based indoor localization |
title_fullStr |
WiFi-based indoor localization |
title_full_unstemmed |
WiFi-based indoor localization |
title_sort |
wifi-based indoor localization |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/156599 |
_version_ |
1731235770783498240 |