iOS indoor positioning study on iPhone platform

• Main Author: Chen, Xucan
• Other Authors: Soong Boon Hee
• Format: Theses and Dissertations
• Language: English
• Published: 2016
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/65882
• Institution: Nanyang Technological University

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.