UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems

Torres-Sospedra, Joaquín; Montoliu, Raúl; Martínez-Usó, Adolfo; Avariento, Joan P.; Arnau, Tomás J.; Benedito-Bordonau, Mauri; Huerta, Joaquín

doi:10.1109/ipin.2014.7275492

Cited by 403 publications

(293 citation statements)

References 31 publications

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“…Reasons behind such contradiction include: (1) reporting the accuracy as mean positioning error distance; (2) testing the IPS in specific (probably controlled) environments; and (3) testing the IPS without considering temporal signals changes. The previous challenges have been addressed, e.g., by: (1) providing other metrics such as 75 percentile instead of the mean [8]; (2) providing databases [9,10]; and (3) periodically updating the IPS training data [11][12][13] or making the positioning method adaptable to signal changes [14,15]. Methods able to cope with temporal signal variation, such as those in Gu et al [14], Hayashi et al [15], are tested with measurements that allow the analysis of short-term signal variations occurred at known positions (e.g., seconds or minutes apart, caused by network devices dynamic behavior, network usage, and people movement) and also the analysis of long-term signal variations (e.g., days or months apart, caused by changes in network devices' configuration or environment' structure).…”

Section: Introductionmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

123

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Abstract:WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

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Section: Introductionmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

123

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“…The data was collected from 30 participants within the age range of 22-79 years. Each activity (standing, sitting, laying, walking, walking upstairs, walking downstairs) was performed for 60 s, and the tri-axial linear acceleration and tri-axial angular velocity were collected at a constant rate of 50 Hz [87].…”

Section: Miscellaneousmentioning

confidence: 99%

“…Thus, it is necessary to find new ways of obtaining fingerprint data quickly and easily, like crowdsourcing [87]. 3.…”

mentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

186

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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“…As such, a dataset of 400 evaluation points is split into 400 sets of 399 training points and one testing point. Similarly, the publicly available UJIIndoor-Loc dataset WiFi fingerprinting dataset [7] consists of 933 reference positions (some of which can be used as evaluation points). Although such a large dataset is very useful, obtaining such large qualities of evaluation points is not feasible for most researchers that want to evaluate their solutions.…”

Section: The Number Of Evaluation Points In Scientific Papersmentioning

confidence: 99%

Benchmarking of Localization Solutions: Guidelines for the Selection of Evaluation Points

Poorter¹,

Haute²,

Laermans³

et al. 2017

Ad Hoc Networks

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Indoor localization solutions are key enablers for next-generation indoor navigation and track and tracing solutions. As a result, an increasing number of different localization algorithms have been proposed and evaluated in scientific literature. However, many of these publications do not accurately substantiate the used evaluation methods. In particular, many authors utilize a different number of evaluation points, but they do not (i) analyze if the number of used evaluation points is sufficient to accurately evaluate the performance of their solutions and (ii) report on the uncertainty of the published results. To remedy this, this paper evaluates the influence of the selection of evaluation points. Based on statistical parameters such as the standard error of the mean value, an estimator is defined that can be used to quantitatively analyze the impact of the number of used evaluation points on the confidence interval of the mean value of the obtained results. This estimator is used to estimate the uncertainty of the presented accuracy results, and can be used to identify if more evaluations are required. To validate the proposed estimator, two different localization algorithms are evaluated in different testbeds and using different types of technology, showing that the number of required evaluation points does indeed vary significantly depending on the evaluated solution.

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UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems

Cited by 403 publications

References 31 publications

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Benchmarking of Localization Solutions: Guidelines for the Selection of Evaluation Points

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