Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Singh, Navneet; Choe, Sangho; Punmiya, Rajiv

doi:10.1109/access.2021.3111083

Cited by 109 publications

(63 citation statements)

References 172 publications

(184 reference statements)

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“…The interest in fingerprinting based indoor localization has received momentum recently due to the adopted potential from the field of deep learning [3]. Fingerprinting based approaches can be differentiated from the device perspective [8].…”

Section: Related Workmentioning

confidence: 99%

“…In that regard, fingerprinting based indoor localization represents an attractive solution for large-scale pedestrian localization [3]. In an offline-phase radio fingerprints detectable in the environment are collected at known reference positions.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the development of the localization algorithm has been dominated by machine learning approaches (especially deep learning). The formulation of the fingerprinting problem can be seen as a classical supervised learning task, where the input data is given by the fingerprints that are labeled either categorically (building/floor/area) or tagged with precise location coordinates [3]. Existing models are mostly optimized for a single task, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Task Neural Network for Position Estimation in Large-Scale Indoor Environments

Laska

Blankenbach

2022

IEEE Access

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Pedestrian localization within large-scale multi-building/multi-floor indoor environments remains a challenging task. Fingerprinting-based approaches are particularly suited for such large-scale deployments due to their low requirements of hardware installments. Recently, the fingerprinting problem has been addressed by deep learning. Existing models are mostly task specific by providing floor classification or position estimation within a small area. A strategy to support localization within large-scale environments is to sequentially apply hierarchical models. This has several drawbacks including missing scalability and increased deployment complexity on smartphones. We propose a unifying approach based on training a single neural network that classifies the building/floor and predicts the position in a single forward-pass of the network. Our model classifies a grid cell and performs within grid cell regression, which solves the performance degradation of applying regression within large areas. To reduce the error in case of misclassified grid cells, we propose a novel technique called multi cell encoding learning (multi-CEL), where a model simultaneously learns several redundant position representations within an overlapping grid cell encoding. For three public WLAN fingerprinting datasets, we demonstrate that multi-CEL surpasses existing state-of-the-art multi-task learning neural networks and even outperforms regression neural networks explicitly trained for 2D-positioning by up to 17%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Task Neural Network for Position Estimation in Large-Scale Indoor Environments

Laska

Blankenbach

2022

IEEE Access

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“…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]. The literature also offers surveys on Machine Learning techniques that leverage Wi-Fi data to face human fall detection [17], human activity recognition [18], smart homes [19], motion detection [20], and human mobility [21].…”

Section: Related Workmentioning

confidence: 99%

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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“…The authors of [14] identified several ways to enhance the radio map, including data cleansing and denoising. In [15], the Received Signal Strength Indicator (RSSI) measurements were extracted to overcome sparsity with a stacked Denoising Au-toEncoder (DAE).…”

Section: Related Workmentioning

confidence: 99%

Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets

Quezada-Gaibor¹,

Klus²,

Torres-Sospedra³

et al. 2022

Preprint

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Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.

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Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Cited by 109 publications

References 172 publications

Multi-Task Neural Network for Position Estimation in Large-Scale Indoor Environments

Multi-Task Neural Network for Position Estimation in Large-Scale Indoor Environments

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

Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets

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