A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

Torres-Sospedra, Joaquín; Moreira, Adriano; Knauth, Stefan; Berkvens, Rafael; Montoliu, Raúl; Belmonte, Óscar; Trilles, Sergio; Nicolau, Maria João; Meneses, Filipe; Costa, António; Koukofikis, Athanasios; Weyn, Maarten; Peremans, Herbert

doi:10.3233/ais-170421

Cited by 37 publications

(22 citation statements)

References 58 publications

(65 reference statements)

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“…The algorithm considers different data representations and distance metrics to mimic the nature of radio propagation, avoid the usage of RSSI as unit-less values and consider the physical underpinning as suggested in [30]. The RTLS@UM system at the EvAAL Competition, which is described in [31,32]: The team from University of Minho (UM), participated in the Evaluating Ambient Assisted Living (EvAAL) competition with their real time location system (RTLS). The proposed base algorithm introduced an iterative procedure to sequentially determine the building, floor and coordinates based on majority rules and kNN/wkNN.…”

Section: Benchmark Indoor Positioning Resultsmentioning

confidence: 99%

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Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Lohan

Torres-Sospedra

Leppäkoski

et al. 2017

Data

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133

104

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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.

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Section: Benchmark Indoor Positioning Resultsmentioning

confidence: 99%

“…A total of more than 21,000 samples was collected by means of more than 20 devices and people. The database was used in the EvAAL ETRICompetition [32]. Approximately 900 reference points were covered.…”

Section: Dataset Repository Refs Main Features and Limitationsmentioning

confidence: 99%

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Lohan

Torres-Sospedra

Leppäkoski

et al. 2017

Data

Self Cite

133

104

View full text Add to dashboard Cite

show abstract

“…The first off-site indoor location competition was the EvAAL-ETRI Indoor Location [35,36], which was held during the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). The third track, called “Wi-Fi fingerprinting in large environments” was off-site, and the competitors had access to a large Wi-Fi fingerprint database, the UJIIndoorLoc [15], to which they had to apply their positioning estimation approaches offline.…”

Section: Introductionmentioning

confidence: 99%

The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work

Torres-Sospedra

Jiménez

Knauth

et al. 2017

Sensors

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This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors’ estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.

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“…One thing to consider in Wi-Fi fingerprinting is how to compare the fingerprint created by the mobile unit with the fingerprints in the database. Torres-Sospedra et al [16] provide an overview of 51 distance metrics that could be used for this comparison. Another consideration in Wi-Fi fingerprinting is the challenge of constructing the fingerprint database.…”

Section: Introductionmentioning

confidence: 99%

Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints

2017

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Featured Application: Localization of a wireless track and trace device communicating over Sigfox and using nearby Wi-Fi access points as references.Abstract: Supply chain management requires regular updates of the location of assets, which can be enabled by low power wide area networks, such as Sigfox. While it is useful to localize a device simply by its communication signals, this is very difficult to do with Sigfox because of wide area and ultra narrowband nature. On the other hand, installing a satellite localization element on the device greatly increases its power consumption. We investigated using information about nearby Wi-Fi access points as a way to localize the asset over the Sigfox network, so without connecting to those Wi-Fi networks. This paper reports the location error that can be achieved by this type of outdoor localization. By using a combination of two databases, we could localize the device on all 36 test locations with a median location error of 39 m. This shows that the localization accuracy of this method is promising enough to warrant further study, most specifically the minimal power consumption.

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A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

Cited by 37 publications

References 58 publications

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work

Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints

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