Platform for benchmarking of RF-based indoor localization solutions

Haute, Tom Van; Poorter, Eli De; Lemić, Filip; Handziski, Vlado; Wirström, Niklas; Voigt, Thiemo; Wolisz, Adam; Moerman, Ingrid

doi:10.1109/mcom.2015.7263356

Cited by 68 publications

(65 citation statements)

References 12 publications

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“…Even though the official evaluation was based on the manual process described in the previous section, the organizers had the ability to leverage the EVARILOS benchmarking platform [16] to automatically evaluate the localization accuracy of the two teams in the infrastructure-based and infrastructure-free categories that achieved the lowest localization errors.…”

Section: Automated Evaluationmentioning

confidence: 99%

A realistic evaluation and comparison of indoor location technologies

Lymberopoulos

Liu

Yang

et al. 2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

Self Cite

308

177

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We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation study's results, discuss the current state-ofthe-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.

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Section: Automated Evaluationmentioning

confidence: 99%

A realistic evaluation and comparison of indoor location technologies

Lymberopoulos

Liu

Yang

et al. 2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

Self Cite

308

177

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show abstract

“…They are created in very different environments. One dataset is created in the w-iLab.t II testing environment, which is a wireless communication testing facility located above a clean room [9]. It is part of the EVARILOS localization benchmarking project, hence the label we use for the dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Our choice of Wi-Fi fingerprinting allows us to use publicly-available datasets. The three datasets are: a dataset from the EVARILOS [9] European project, in the w-iLab.t II environment; GEOTEC [10], a smaller database in an office environment; and UJIIndoorLoc [11], a large dataset created in three buildings of up to four floors.…”

Section: Introductionmentioning

confidence: 99%

Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting

Berkvens

Peremans

Weyn

2016

Sensors

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Localization systems are increasingly valuable, but their location estimates are only useful when the uncertainty of the estimate is known. This uncertainty is currently calculated as the location error given a ground truth, which is then used as a static measure in sometimes very different environments. In contrast, we propose the use of the conditional entropy of a posterior probability distribution as a complementary measure of uncertainty. This measure has the advantage of being dynamic, i.e., it can be calculated during localization based on individual sensor measurements, does not require a ground truth, and can be applied to discrete localization algorithms. Furthermore, for every consistent location estimation algorithm, both the location error and the conditional entropy measures must be related, i.e., a low entropy should always correspond with a small location error, while a high entropy can correspond with either a small or large location error. We validate this relationship experimentally by calculating both measures of uncertainty in three publicly available datasets using probabilistic Wi-Fi fingerprinting with eight different implementations of the sensor model. We show that the discrepancy between these measures, i.e., many location estimates having a high location error while simultaneously having a low conditional entropy, is largest for the least realistic implementations of the probabilistic sensor model. Based on the results presented in this paper, we conclude that conditional entropy, being dynamic, complementary to location error, and applicable to both continuous and discrete localization, provides an important extra means of characterizing a localization method.

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“…There [38], and the EVARILOS project and competition [91]. Besides, [92,93] introduced the benchmarking tools for indoor positioning solutions.…”

Section: Miscellaneousmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

205

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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Platform for benchmarking of RF-based indoor localization solutions

Cited by 68 publications

References 12 publications

A realistic evaluation and comparison of indoor location technologies

A realistic evaluation and comparison of indoor location technologies

Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

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