Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets

Quezada-Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell, Carlos; Huerta, Joaquín

doi:10.1109/mdm55031.2022.00079

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…Excluding these WAPs could substantially reduce the input dimension of indoor localization models and storage requirements for the database, particularly for large-scale scenarios. There have been proposed several WAP selection schemes [41]- [44], but their application to large-scale multi-building and multi-floor indoor localization databases are to be carefully investigated.…”

Section: Challenges and Recommendationsmentioning

confidence: 99%

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database

Li,

Tang,

Kim

et al. 2024

Preprint

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Section: Challenges and Recommendationsmentioning

confidence: 99%

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database

Li,

Tang,

Kim

et al. 2024

Preprint

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“…To this end, they proposed Kullback–Leibler divergence (KLD) to characterize the difference between the real distribution and the sampling distribution. Quezada-Gaibor et al proposed a data cleaning algorithm based on the correlation among all samples in the fingerprint database [ 56 ]. The correlation among samples was calculated by the correlation between the RSSI fingerprint and AP’s identifier.…”

Section: Wi-fi-assisted Schemes On Different Principlesmentioning

confidence: 99%

A Survey of Latest Wi-Fi Assisted Indoor Positioning on Different Principles

Dai,

Wang,

et al. 2023

Sensors

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As the location-based service (LBS) plays an increasingly important role in real life, the topic of positioning attracts more and more attention. Under different environments and principles, researchers have proposed a series of positioning schemes and implemented many positioning systems. With widely deployed networks and massive devices, wireless fidelity (Wi-Fi) technology is promising in the field of indoor positioning. In this paper, we survey the authoritative or latest positioning schemes for Wi-Fi-assisted indoor positioning. To this end, we describe the problem and corresponding applications, as well as an overview of the alternative methods. Then, we classify and analyze Wi-Fi-assisted indoor positioning schemes in detail, as well as review related work. Furthermore, we point out open challenges and forecast promising directions for future work.

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“…While a variety of indoor positioning technologies exist, positioning based on Wi-Fi fingerprinting [26] is popular: the ubiquity of Wi-Fi enables positioning without the deployment of additional expensive infrastructure, and the technology is non-intrusive to users. However, the accuracy of Wi-Fi fingerprinting based positioning depends heavily on the quality of the radio map data used [38], [46], [52], [55].…”

Section: Introductionmentioning

confidence: 99%

Data Imputation for Sparse Radio Maps in Indoor Positioning (Extended Version)

Li¹,

Li²,

Chan³

et al. 2023

Preprint

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Indoor location-based services rely on the availability of sufficiently accurate positioning in indoor spaces. A popular approach to positioning relies on so-called radio maps that contain pairs of a vector of Wi-Fi signal strength indicator values (RSSIs), called a fingerprint, and a location label, called a reference point (RP), in which the fingerprint was observed. The positioning accuracy depends on the quality of the radio maps and their fingerprints. Radio maps are often sparse, with many pairs containing vectors missing many RSSIs as well as RPs. Aiming to improve positioning accuracy, we present a complete set of techniques to impute such missing values in radio maps. We differentiate two types of missing RSSIs: missing not at random (MNAR) and missing at random (MAR). Specifically, we design a framework encompassing a missing RSSI differentiator followed by a data imputer for missing values. The differentiator identifies MARs and MNARs via clustering-based fingerprint analysis. Missing RSSIs and RPs are then imputed jointly by means of a novel encoder-decoder architecture that leverages temporal dependencies in data collection as well as correlations among fingerprints and RPs. A time-lag mechanism is used to consider the aging of data, and a sparsity-friendly attention mechanism is used to focus attention score calculation on observed data. Extensive experiments with real data from two buildings show that our proposal outperforms the alternatives with significant advantages in terms of imputation accuracy and indoor positioning accuracy.

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Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets

Cited by 12 publications

References 20 publications

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database

A Survey of Latest Wi-Fi Assisted Indoor Positioning on Different Principles

Data Imputation for Sparse Radio Maps in Indoor Positioning (Extended Version)

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