Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Tran, Huy Q.; Ha, Cheolkeun

doi:10.3390/app9061048

Cited by 48 publications

(30 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar solutions have been proposed based on various classification methods over the received signal strengths of lights [73][74][75][76][77][78] recently. Both [73] and [74] also used the KNN classifier for their localization algorithms.…”

Section: Light Intensity As Fingerprintsmentioning

confidence: 99%

“…The latter one relies on the signal features from the flicker frequency spectra followed by KNN clustering. In [75], multiple classifiers were leveraged, and two fusion localization algorithms were proposed (i.e., grid-independent and grid-dependent least square) to combine the outputs of multiple trained classifiers, whereas [77] used two popular functions (classification and regression) of ML-based algorithms (such as KNN, decision trees (DT), support vector machines (SVM), and random forest (RF)). On the other hand, [76] and [78] leveraged neural networks in their VLL systems.…”

Section: Light Intensity As Fingerprintsmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Indoor Localization via Visible Lights: A Survey

Rahman

Wang

2020

Sensors

109

View full text Add to dashboard Cite

Because of the limitations of the Global Positioning System (GPS) in indoor scenarios, various types of indoor positioning or localization technologies have been proposed and deployed. Wireless radio signals have been widely used for both communication and localization purposes due to their popular availability in indoor spaces. However, the accuracy of indoor localization based purely on radio signals is still not perfect. Recently, visible light communication (VLC) has made use of electromagnetic radiation from light sources for transmitting data. The potential for deploying visible light communication for indoor localization has been investigated in recent years. Visible-light-based localization enjoys low deployment cost, high throughput, and high security. In this article, the most recent advances in visible-light-based indoor localization systems have been reviewed. We strongly believe that visible-light-based localization will become a low-cost and feasible complementary solution for indoor localization and other smart building applications.

show abstract

Section: Light Intensity As Fingerprintsmentioning

confidence: 99%

Section: Light Intensity As Fingerprintsmentioning

confidence: 99%

Recent Advances in Indoor Localization via Visible Lights: A Survey

Rahman

Wang

2020

Sensors

109

View full text Add to dashboard Cite

show abstract

“…WLAN, RFID, and Bluetooth are among the RF-based techniques. Audio, visual, ultra-sonic, Infra-Red (IR), laser sensors, and magnetic field are considered as non-RF-based methods [15][16][17][18]. In this article, we mainly focus on RF-based techniques.…”

Section: Related Workmentioning

confidence: 99%

RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems

Abed

Abdel‐Qader

2019

Applied Sciences

View full text Add to dashboard Cite

Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy <0.85 m over 3000 m2, with an accumulative density function (CDF) of 88% with a distance error of 2 m.

show abstract

“…Among these VLP schemes, VLP systems based on received-signal-strength (RSS) have also received much attention [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. In these RSS based VLP systems, the received optical power depended on the distance between Tx and Rx.…”

Section: Introductionmentioning

confidence: 99%

Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method

Chow

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this work, we propose and demonstrate a received-signal-strength (RSS) based visiblelight-positioning (VLP) system using sigmoid function data preprocessing (SFDP) method; and apply it to two types of regression based machine learning algorithms; including the second-order linear regression machine learning (LRML) algorithm, and the kernel ridge regression machine learning (KRRML) algorithm. Experimental results indicate that the use of SFDP method can significantly improve the positioning accuracies in both the LRML and KRRML algorithms. Besides, the SFDP with KRRML scheme outperforms the other three schemes in terms of position accuracy, with the experimental average positioning error of about 2 cm in both horizontal and vertical directions. INDEX TERMS Visible light communication (VLC), visible light positioning (VLP), light-emitting-diode (LED), machine learning.

show abstract

Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Cited by 48 publications

References 31 publications

Recent Advances in Indoor Localization via Visible Lights: A Survey

Recent Advances in Indoor Localization via Visible Lights: A Survey

RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems

Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method

Contact Info

Product

Resources

About