Paper Info
Title
Anatomy and Deployment of Robust AI-Centric Indoor Positioning System
Abstract
Indoor Positioning Systems are gaining market momentum, mainly due to the significant reduction of sensor cost (on smartphones or standalone) and leveraging standardization of related technology. Among various alternatives for accurate and cost-effective Indoor Positioning System, positioning based on the Magnetic Field has proven popular, as it does not require specialized infrastructure. Related experimental results have demonstrated good positioning accuracy. However, when transitioned to production deployments, these systems exhibit serious drawbacks to make them practical: a) accuracy fluctuates significantly across smartphone models and configurations and b) costly continuous manual fingerprinting of the area is required. The developed Indoor Positioning System Copernicus is a self-learning, adaptive system that is shown to exhibit improved accuracy across different smartphone models. Copernicus leverages a minimal deployment of Bluetooth Low Energy Beacons to infer the trips of users, learn and eventually build tailored Magnetic Maps for every smartphone model for the specific indoor area. In a practical deployment, after each trip execution by the users we can observe an increase in the accuracy of positioning.
Year
DOI
Venue
2019
10.1109/PERCOMW.2019.8730798
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
Keywords
Field
DocType
Smart phones,Magnetosphere,Magnetometers,Magnetic recording,Magnetic fields,Bluetooth,Magnetic resonance imaging
Beacon,Software deployment,Adaptive system,Computer science,Standardization,Indoor positioning system,Bluetooth,Bluetooth Low Energy,Distributed computing
Conference
ISSN
ISBN
Citations 
2474-2503
978-1-5386-9151-9
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Yiannis Gkoufas1175.96
Stefano Braghin282.56