Mining raw GPS readings for deep profiling of location contexts - part II

• Main Author: Lim, Zi Hao
• Other Authors: Luo Jun
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science
• Online Access: https://hdl.handle.net/10356/175281
• Institution: Nanyang Technological University

This project explores the usage of raw GPS readings to deeply profile location contexts, with a specific focus on distinguishing between indoor and outdoor locations. The research leverages on the Global Navigation Satellite System (GNSS) to collect raw GNSS data using Android devices. The data was collected from various environments in Singapore during the day. This study aims to overcome the challenges posed by GNSS signal degradation in complex environments and attempts to identify a way to accurately profile indoor and outdoor location contexts. This is done by using GNSSLogger to collect raw GNSS data followed by data processing to filter out invalid coordinates. The results demonstrated the potential of raw GNSS data that can provide insights into location contexts. The findings highlight the importance of improving data collection methods and integrating GNSS data with other positioning or profiling techniques to achieve more reliable and accurate location profiling.