Dataset for "Advancements in Indoor Floor-Level Detection: A Comprehensive Algorithm for Vertical Positioning in Multi-Building Environments"
This repository contains the dataset used in the research for the article titled "Advancements in Indoor Floor-Level Detection: A Comprehensive Algorithm for Vertical Positioning in Multi-Building Environments". The dataset is organized to support and validate the research findings presented in the paper.
- Rafał Marjasz
- Krzysztof Grochla
- Konrad Połys
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences
The proliferation of smartphones has catalysed diverse services, mainly focusing on indoor localization to determine users' and devices' positions within buildings. Despite decades of exploration, the seamless integration of wireless technologies in tracking devices and users has become pivotal in various sectors, including health, industry, disaster management, building operations, and surveillance. Extensive research in laboratory and industrial settings, particularly in wireless sensor networks and robotics, has informed indoor localization techniques. This paper, referencing surveys and original literature reviews, proposes an innovative indoor location system amalgamating GPS and barometer readings. GPS identifies entry through doors, while barometer readings facilitate accurate floor-level tracking. The integration promises continuous real-time location updates, enhancing security, navigation, and emergency response. Notably, the algorithm is infrastructure-independent, relying on the smartphone's barometer, and versatile, detecting elevator travel when Wi-Fi AP or LTE signals are available. Results indicate high accuracy, with building entry exceeding 93%, elevator recognition achieving 75% sensitivity and 97% specificity, and floor change detection surpassing 95% sensitivity and nearly 98% specificity. This comprehensive solution, emphasizing the critical role of precise vertical positioning, signifies an advancement in tracking within urban structures.
The dataset is composed of CSV files where each file's name indicates the type of data it contains. Each CSV file is structured with columns, and each column has a self-explanatory name that clearly describes the data it holds. This design ensures that the dataset is intuitive and accessible for researchers to analyze and utilize.