Wi-Fi Fingerprint Localization Based on Multi-output Least Square Support Vector Regression

Malar, A. Christy Jeba; Priya, M. Deva; Femila, F.; Peter, S. Sam; Ravi, Viraja

doi:10.1007/978-981-33-6081-5_49

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The authors in [ 33 ] integrated a statistical hypothesis test on asymptotic relative efficiency (ARE) to optimize signal distribution at the site coverage area. Another work [ 34 ] introduces multi-output least square support vector machine (M-LS-SVM) regression to improve classification of RSS fingerprint data. Localization in [ 35 ] is achieved by fusion of grid-independent and grid-dependent least-square classifications.…”

Section: Related Workmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

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Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

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Section: Related Workmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

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show abstract

“…Ref. [33] shows an M-LS-SVM algorithm, which is characterized by the use of linear functions instead of the quadratic functions of the original SVM; the authors obtained an accuracy of 2.7 m. However, [34] used the SVM algorithm directly, obtaining an accuracy of 0.7 m in a similar scenario, and [35] used a SVM algorithm with CSI instead of Received Signal Strength Indicator (RSSI) and obtained an accuracy of 1.909 m in a simpler scenario with no rooms or obstacles.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review

Bellavista-Parent

Torres-Sospedra

Pérez-Navarro

2022

Sensors

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Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines.

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Indoor Navigation for Mobile Devices using Augmented Reality with Wi-Fi RTT

Christy Jeba Malar,

Deva Priya,

Karthick

et al. 2024

Proceedings of International Conference on Recent Trends in Computing

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Wi-Fi Fingerprint Localization Based on Multi-output Least Square Support Vector Regression

Cited by 4 publications

References 14 publications

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review

Indoor Navigation for Mobile Devices using Augmented Reality with Wi-Fi RTT

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