Wi-Fi Based Indoor Positioning System with Using Deep Neural Network

Giney, Selda; Erdogan, Alperen; Aktaş, Melih; Ergun, Mert

doi:10.1109/tsp49548.2020.9163548

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Next, to accomplish precise localization in Wi-Fi situations, researchers suggested using DNNs (Adege et al, 2018.). Giney et al (2020) used DNN and more traditional machine learning algorithms for categorizing 22-square grids representing different places.…”

Section: Discussion and Analysis Of Deep Learning Algorithmmentioning

confidence: 99%

“…The effectiveness of a DNN is proportional to the sum of its hidden layer sizes Applying deep neural network (DNN) for robust indoor localization in multibuilding environments (Adege et al, 2018. ) 2018 DNN DNN is capable of accurately localizing Wi-Fi users in wireless environments that are both hierarchical and complex Wi-Fi-based indoor positioning system using deep neural network (Giney et al, 2020)…”

Section: Dnnmentioning

confidence: 99%

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A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

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The term “indoor navigation system” pertains to a technological or practical approach that facilitates the navigation and orientation of individuals within indoor settings, such as museums, airports, shopping malls, or buildings. Over several years, significant advancements have been made in indoor navigation. Numerous studies have been conducted on the issue. However, a fair evaluation and comparison of indoor navigation algorithms have not been discussed further. This paper presents a comprehensive review of collective algorithms developed for indoor navigation. The in-depth analysis of these articles concentrates on both advantages and disadvantages, as well as the different types of algorithms used in each article. A systematic literature review (SLR) methodology guided our article-finding, vetting, and grading processes. Finally, we narrowed the pool down to 75 articles using SLR. We organized them into several groups according to their topics. In these quick analyses, we pull out the most important concepts, article types, rating criteria, and the positives and negatives of each piece. Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system.

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Section: Discussion and Analysis Of Deep Learning Algorithmmentioning

confidence: 99%

Section: Dnnmentioning

confidence: 99%

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

View full text Add to dashboard Cite

show abstract

“…Other metrics used are the mean squared error (MSE) and the median error (MedianError). On the other hand, 14 papers report the error of success in a certain precision based in percentage [16,23,28,29,36,42,47,48,57,52,53,67,54,60]. Unfortunately, three articles do not show results clearly or are based on items different from those analyzed in this review [79,45,50].…”

Section: Metricsmentioning

confidence: 94%

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

Bellavista-Parent,

Torres-Sospedra,

Perez-Navarro

2021

Preprint

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Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.

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“…Other metrics used are the Mean Squared Error (MSE) and the Median Error. Another important metric is the percentage, which is used in one way or another in 36 papers [22,24,25,[55][56][57][58][59][60][61][62][63][64][65]. Unfortunately, two articles do not show their results clearly; papers [66,67] only show a graph, however, it is difficult to determine the obtained results from the image.…”

Section: Evaluation Metricsmentioning

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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Wi-Fi Based Indoor Positioning System with Using Deep Neural Network

Cited by 8 publications

References 10 publications

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

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

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

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