Feature selection on database optimization for Wi-Fi fingerprint indoor positioning

Apostolo, Guilherme Henrique; Sampaio, Igor Garcia Ballhausen; Viterbo, José

doi:10.1016/j.procs.2019.09.180

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…Literature research faces two key problems in fingerprint based localization. Firstly, the observed RSS vectors contain a large number of missing values due to the obstruction of out-of-range APs, random noise, signal fluctuation or scanning duration [ 7 ], especially inside large buildings, such as shopping malls and hospitals, which results in extreme data sparsity. Traditional data dimensionality reduction methods, including principal component analysis (PCA) [ 8 ] and linear discriminant analysis (LDA) [ 9 ], treat all samples as a whole to find an optimal linear mapping projection with the smallest mean square error.…”

Section: Introductionmentioning

confidence: 99%

JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM

Yin

Deng

2021

Sensors

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Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, with the localization system based on received signal strength (RSS), is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm—named joint denoising auto-encoder (JDAE)—which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on the UJIIndoorLoc dataset and the Tampere dataset, the experimental results show that the proposed model increases the positioning accuracy dramatically compared with other existing methods.

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Section: Introductionmentioning

confidence: 99%

JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM

Yin

Deng

2021

Sensors

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“…Local trust zones [318]- [322] Massive twinning [323], [324] Sustainable develop. [325]- [327] Indoor [318]- [320], [323], [325]- [327] Outdoor [321], [322], [324] Radars [318], [323], [324] Sensors [319], [320], [325]- [327] mmWave/THz [321], [322] AoA, RSS, RMSE…”

Section: Decision Treesmentioning

confidence: 99%

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Trevlakis¹,

Boulogeorgos²,

Pliatsios³

et al. 2023

Preprint

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<p>When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learningbased localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.</p>

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“…Jia, Huang, Gao et al prove that when the number of APs increases beyond a certain threshold, localization performance can not be significantly improved [14], providing theoretical support for feature selection in fingerprinting. In paper [15], 30 strongest APs as well as their ID are selected as new features. AP selection can also be performed by minimizing the correlation of selected APs [16].…”

Section: Related Workmentioning

confidence: 99%

Hierarchical fingerprinting and feature extraction for indoor localization

Liu

Lacerda

Fiorina

2022

ICC 2022 - IEEE International Conference on Communications

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Weighted K nearest neighbor (WKNN) algorithm provides good result for indoor localization by searching for matching fingerprints in the dataset. However, due to the nature of this method, computation load increases as the size of dataset. In this paper, we propose a hierarchical localization method to reduce the computation load during the online phase. A twolevel structure first localizes the user to a smaller subset and then position estimation is obtained by WKNN with feature extraction. On a public accessed WiFi fingerprinting dataset, the proposed method achieves a 98% reduction without sacrificing localization performance.

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Feature selection on database optimization for Wi-Fi fingerprint indoor positioning

Cited by 10 publications

References 4 publications

JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM

JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

Hierarchical fingerprinting and feature extraction for indoor localization

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