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Abstract
Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository.
Highlights
Introduction and MotivationWireless Local Area Networks (WLANs), called Wi-Fi, are widespread in urban scenarios in order to enable and support broadband communications
While there is a general understanding in the research community that Wi-Fi-based positioning can reach a positioning accuracy down to a few meters, very few comparative studies of algorithms tested under various datasets exist, and very few benchmark open-source Wi-Fi datasets for indoor positioning purposes have been made available to the research community so far, to the best of the authors’ knowledge
This paper has introduced a new database for Wi-Fi-based indoor positioning
Summary
Wireless Local Area Networks (WLANs), called Wi-Fi, are widespread in urban scenarios in order to enable and support broadband communications. One of the solutions to address this current lack of benchmarks for indoor positioning is to offer open-source data collected from multi-floor multi-corridor buildings freely to the research community and to add some illustrative results obtained with such a data, in order to create a starting point, or baseline, for comparison of various indoor positioning algorithms This solution is addressed in our paper with a dataset collected during January–August 2017 at Tampere University of Technology, based on an Android application created for this purpose and involving several volunteers with various Android devices to collect the data. To the best of the authors’ knowledge, there is no open-source Wi-Fi database with the following features: collected in a full crowdsourced mode (i.e., different devices, different users and no main indications), well documented with good data description, tested with many different algorithms (comprehensive benchmarking) and providing collection and utility software. Datasets.html; IndoorLoc: http://indoorlocplatform.uji.es/; CRAWDAD: http://crawdad.org; TUT repo: http://www.cs.tut.fi/tlt/pos/Software.htm
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