GraphIPS: Calibration-Free and Map-Free Indoor Positioning Using Smartphone Crowdsourced Data
| Autor: | Wai-Choong Wong, Hari Krishna Garg, Tianyi Feng, Zhixiang Zhang, Yonghao Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science 010401 analytical chemistry Real-time computing Process (computing) Wearable computer 020206 networking & telecommunications 02 engineering and technology Construct (python library) Simultaneous localization and mapping 01 natural sciences 0104 chemical sciences Computer Science Applications Hardware and Architecture Inertial measurement unit Signal Processing Dead reckoning 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 8:393-406 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2020.3004703 |
| Popis: | Indoor positioning plays an important role in a variety of applications under Internet of Things (IoT). Conventional WiFi fingerprinting-based indoor positioning systems (IPSs) usually require extensive manual calibrations to construct radio maps. This process severely limits the system scalability and adaptiveness. Pedestrian dead reckoning (PDR) is a popular method that can avoid the calibration process. However, PDR-based IPSs typically suffer from accumulated errors. To tackle this problem, many refinement methods require map information or floorplans which may not be available or up-to-date in practice. With the development of IoT, various types of crowdsourced data become available. In this work, we propose GraphIPS, a calibration-free and map-free IPS which dynamically generates accurate radio maps by utilizing smartphone crowdsourced WiFi and inertial measurement unit (IMU) data. GraphIPS fuses the crowdsourced data into a graph-based formulation and applies the multidimensional scaling (MDS) algorithm to compute the positions of the user’s steps. The experimental results show that GraphIPS achieves comparable accuracy to the calibration-based method in a significantly shorter run time than optimization-based methods. In addition to smartphones, GraphIPS is also potentially applicable for the smart wearables with embedded WiFi modules and IMUs. |
| Databáze: | OpenAIRE |
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