iOS indoor positioning study on iPhone platform
This dissertation aims to assist readers have a better understanding of the steps and procedures of the indoor positioning system. Beacon is a cutting-edge technology device announced by Apple and it is often used as tags in this project for indoor localization. It can detect IOS devices within a...
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/65882 |
| _version_ | 1811695322873724928 |
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| author | Chen, Xucan |
| author2 | Soong Boon Hee |
| author_facet | Soong Boon Hee Chen, Xucan |
| author_sort | Chen, Xucan |
| collection | NTU |
| description | This dissertation aims to assist readers have a better understanding of the steps and procedures of the indoor positioning system.
Beacon is a cutting-edge technology device announced by Apple and it is often used as tags in this project for indoor localization. It can detect IOS devices within a specified range. Bluetooth low-energy devices can advertise iBeacon information. When IOS devices are close to the BLE devices, those IOS devices can detect the signals.
This dissertation discussed two popular algorithms for the IOS platform. (i) trilateration method and (ii) fingerprint-based method. The challenge of the project was to improve the accuracy. A series of tests were conducted to compare the two algorithms on the iPhone on some simple paths. It was found the fingerprint method has better accuracy. Furthermore, we designed the web server and created the database to collect the reference points RSS value and connected the application to the web server. The localization function is realized by matching the reference RSS value with received RSS value.
The application can detect the iBeacon signals and will match them with data in the cloud server. The map can show the position of the users. |
| first_indexed | 2024-10-01T07:21:38Z |
| format | Thesis |
| id | ntu-10356/65882 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:21:38Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/658822023-07-04T15:49:02Z iOS indoor positioning study on iPhone platform Chen, Xucan Soong Boon Hee School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering This dissertation aims to assist readers have a better understanding of the steps and procedures of the indoor positioning system. Beacon is a cutting-edge technology device announced by Apple and it is often used as tags in this project for indoor localization. It can detect IOS devices within a specified range. Bluetooth low-energy devices can advertise iBeacon information. When IOS devices are close to the BLE devices, those IOS devices can detect the signals. This dissertation discussed two popular algorithms for the IOS platform. (i) trilateration method and (ii) fingerprint-based method. The challenge of the project was to improve the accuracy. A series of tests were conducted to compare the two algorithms on the iPhone on some simple paths. It was found the fingerprint method has better accuracy. Furthermore, we designed the web server and created the database to collect the reference points RSS value and connected the application to the web server. The localization function is realized by matching the reference RSS value with received RSS value. The application can detect the iBeacon signals and will match them with data in the cloud server. The map can show the position of the users. Master of Science (Communications Engineering) 2016-01-11T02:15:33Z 2016-01-11T02:15:33Z 2016 Thesis http://hdl.handle.net/10356/65882 en 75 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Chen, Xucan iOS indoor positioning study on iPhone platform |
| title | iOS indoor positioning study on iPhone platform |
| title_full | iOS indoor positioning study on iPhone platform |
| title_fullStr | iOS indoor positioning study on iPhone platform |
| title_full_unstemmed | iOS indoor positioning study on iPhone platform |
| title_short | iOS indoor positioning study on iPhone platform |
| title_sort | ios indoor positioning study on iphone platform |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/65882 |
| work_keys_str_mv | AT chenxucan iosindoorpositioningstudyoniphoneplatform |