PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Barsocchi P., Crivello A., La Rosa D., Palumbo F. A multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting. In: IPIN 2016 - International Conference on Indoor Positioning and Indoor Navigation (Alcalá de Henares, Madrid, Spain, 4-7 October 2016). Proceedings, article n. 7743678. IEEE, 2016.
 
 
Abstract
(English)
Indoor localization is a key topic for the Ambient Intelligence (AmI) research community. In this scenarios, recent advancements in wearable technologies, particularly smartwatches with built-in sensors, and personal devices, such as smartphones, are being seen as the breakthrough for making concrete the envisioned Smart Environment (SE) paradigm. In particular, scenarios devoted to indoor localization represent a key challenge to be addressed. Many works try to solve the indoor localization issue, but the lack of a common dataset or frameworks to compare and evaluate solutions represent a big barrier to be overcome in the field. The unavailability and uncertainty of public datasets hinders the possibility to compare different indoor localization algorithms. This constitutes the main motivation of the proposed dataset described herein. We collected Wi-Fi and geo-magnetic field fingerprints, together with inertial sensor data during two campaigns performed in the same environment. Retrieving sincronized data from a smartwatch and a smartphone worn by users at the purpose of create and present a public available dataset is the goal of this work.
URL: http://ieeexplore.ieee.org/document/7743678
DOI: 10.1109/IPIN.2016.7743678
Subject Dataset
Indoor Localization
Geomagnetic Field
Fingerprinting
C.2.1 Network Architecture and Design


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional