New trends in indoor positioning based on WiFi and machine learning: A systematic review

Bellavista-Parent, Vladimir; Torres-Sospedra, Joaquín; Pérez-Navarro, Antoni

doi:10.1109/ipin51156.2021.9662521

Cited by 21 publications

(12 citation statements)

References 79 publications

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“…These works give different points of view and perspectives. Some of them are more general [12][13][14] and differ from each other mainly for the review's procedure. Others analyze in more detail the data source, differentiating between channel state information [15] and received signal strength [16].…”

Section: Related Workmentioning

confidence: 99%

“…The analyzed articles have been downloaded on the 14th of March 2022 from three different sources, the Scopus [52] and the Web of Science [53] databases, and the IEEE Xplore digital library [54]. All of these sources are quite popular and frequently updated; they offer APIs to easily query them and have been used for similar works in the past [12]. The Scopus and IEEE Xplore search engines allow searching for strings inside titles, abstracts, and keywords; the Web of Sciences search engine allows only searching inside the abstract.…”

Section: Data Retrieving and Screeningmentioning

confidence: 99%

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A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Atzeni

Bacciu

Mazzei

et al. 2022

Sensors

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Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.

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Section: Related Workmentioning

confidence: 99%

Section: Data Retrieving and Screeningmentioning

confidence: 99%

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Atzeni

Bacciu

Mazzei

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…This article is an extension of a work presented at the 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021) [11]. Its novel contents include the following:…”

Section: Introductionmentioning

confidence: 99%

“…The current work extends the analyzed period to the last five years, analysing a total of 119 published research works, 57 more than in [11]; • An analysis of solutions based on Artificial Neural Networks (ANN), Suport Vector Machines (SVM), and Random Forest (RF) is included; • A comprehensive analysis of the most widely used public datasets (radio maps) and how they have been integrated in experiments performed by the research community; • A discussion of the size of the operational areas considered in experiments performed in the reviewed works; • Extended context, discussion, and conclusions.…”

mentioning

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

Self Cite

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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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“…The mapping between fingerprints and locations can be predicted by an algorithm. Recently, machine learning (ML) has been extensively used for this purpose [3]. Training a supervised ML algorithm requires fingerprints labeled with the corresponding position.…”

Section: Introductionmentioning

confidence: 99%

VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization

Laska¹,

Schulz²,

Grottke³

et al. 2022

Preprint

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Fingerprinting-based approaches are particularly suitable for deploying indoor positioning systems for pedestrians with minimal infrastructure costs. The accuracy of the method, however, strongly depends on the quality of collected labeled fingerprints within the calibration phase, which is a tedious process when done manually in a static fashion. We present VI-SLAM2tag, a system for auto-labeling of dynamically collected fingerprints using the visual-inertial simultaneous localization and mapping (VI-SLAM) module of ARCore. ARCore occasionally updates its internal coordinate system. Mapping the entire trajectory to a target coordinate system via a single transformation thus results in large drift effects. To solve this, we propose a strategy for determining locally optimal subtrajectory transformations. Our system is evaluated with respect to the accuracy of the generated position labels using a geodetic tracking system. We achieve an average labeling error of roughly 50 cm for trajectories of up to 15 minutes, which is sufficient for fingerprinting-based localization. We demonstrate this by collecting a multi-floor dataset including WLAN and IMU data and show how it can be used to train neural network based models that achieve a positioning accuracy of roughly 2 m. VI-SLAM2tag and the dataset are made publicly available.

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New trends in indoor positioning based on WiFi and machine learning: A systematic review

Cited by 21 publications

References 79 publications

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

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

VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization

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