Abstract
Indoor Positioning is a crucial topic to provide autonomous services to people based on their location. Nowadays dominating positioning systems, like GPS (Global Positioning System), are designed for outdoor use not applicable for indoor scenarios as they depend on a direct line of sight to reference stations. Recent progress in wearable computing peaked in the promising development of SmartWatches. They are seen as a successor of the SmartPhone evoking a new era of an always on, large scale, planet spanning, body sensor network. This work investigates in the question if SmartWatches are an accurate and suitable approach for an out of the lab, 24/7, real world Indoor Positioning System. In utilising Wi-Fi fingerprinting methodologies in combination with machine learning techniques, it is shown that state of the art consumer hardware in form of SmartWatches can be used to shape a cost effective, unobtrusive, and accurate indoor positioning system.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) |
| Publisher | IEEE |
| Pages | 703-708 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509043385 |
| DOIs | |
| Publication status | Published - May 2017 |
Fields of science
- 102 Computer Sciences
- 102009 Computer simulation
- 102013 Human-computer interaction
- 102019 Machine learning
- 102021 Pervasive computing
- 102022 Software development
- 102025 Distributed systems
JKU Focus areas
- Computation in Informatics and Mathematics
- Engineering and Natural Sciences (in general)