Deep Learning Based Data Recovery for Localization

Njima, Wafa; Chafii, Marwa; Nimr, Ahmad; Fettweis, Gerhard

doi:10.1109/access.2020.3026615

Cited by 24 publications

(22 citation statements)

References 41 publications

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“…In [120], the authors proposed a technique using a stacked denoising autoencoder (DAE) that extracts RSSI measurements to overcome the sparsity of Wi-Fi signals. Another neural network scheme proposed by the authors in [121] handles sparsity and fluctuations in indoor areas and efficiently executes data denoising. In [122], the authors proposed a denoiser that learns noise characteristics instead of learning original data characteristics.…”

Section: Techniques For Construction and Enrichment Of Radio Mapsmentioning

confidence: 99%

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

Choe²,

2021

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In the era of the Internet of Things (IoT) and Industry 4.0, the indoor usage of smart devices is expected to increase, thereby making their location information more important. Based on various practical issues related to large delays, high design cost, and limited performance, conventional localization techniques are not practical for indoor IoT applications. In recent years, many researchers have proposed a wide range of machine learning (ML)-based indoor localization approaches using Wi-Fi received signal strength indicator (RSSI) fingerprints. This survey attempts to provide a summarized investigation of MLbased Wi-Fi RSSI fingerprinting schemes, including data preprocessing, data augmentation, ML prediction models for indoor localization, and postprocessing in ML, and compare their performance. Any ML-based study is heavily reliant on datasets. Therefore, we dedicate a significant portion of this survey to the discussion of dataset collection and open-source datasets. To provide good direction for future research, we discuss the current challenges and potential solutions related to ML-based indoor localization systems.

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Section: Techniques For Construction and Enrichment Of Radio Mapsmentioning

confidence: 99%

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

Choe²,

2021

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“…There are several methods to solve the problem of signal indicator scattering for indoor localization purposes. They are the Euclidean Distance Matrix (EDM) [26] and the semi-sequential probabilistic model (SSP) [27].…”

Section: Interferencementioning

confidence: 99%

Comparison of Wireless Adapters Interference Based on Differences of Floor Position

Arnomo¹,

Yulia²,

Ibrahim

2021

Jurnal Teknologi

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The wireless network adapter has now been widely developed. The wireless adapter receives signal strength has several levels. It depends on the signal strength of the device with respect to the wireless signal and access point. The external adapter is used by computers that are not equipped with a wireless network adapter. Usually, internet users use a USB wireless adapter. An external adapter can also amplify signal reception from hotspots. The problem is how users consider using an external wireless adapter or just an onboard wireless adapter when there are many networks interfering with the weak signal strength of the access point. The method is implemented by measuring the value of the Received Signal Strength Indicator (RSSI). The purpose of this study is to compare the received signal strength in conditions where the signal before interference and after experiencing interference (interference). Meanwhile, the measured RSSI value is divided into four floors and with different distances for each floor. Each floor is measured by five distances. The average value of signal strength at a distance of 10 meters is -74 dBm using the onboard wireless adapter and -69 dBm using a USB wireless adapter. The value obtained after the interference is -75 dBm and the USB wireless adapter gets -61 dBm. The reduction of the RSSI value between interference and non-interference is 3%. The onboard wireless adapter is affected by interference while the external wireless adapter is not affected.

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“…The indoor localization problem can be classified into two principal branches: machine learning methods [18][19][20][21][22][23][24][25][26][27][28][29] and filters-based methods [30][31][32][33][34][35][36][37]. Traditional supervised machine learning methods such as SVM (support vector machine), KNN (k-nearest neighbors), Naive Bayes, and decision tree methods are capable of solving the data extraction, matching, and notably classification issues on the localization problem.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional supervised machine learning methods such as SVM (support vector machine), KNN (k-nearest neighbors), Naive Bayes, and decision tree methods are capable of solving the data extraction, matching, and notably classification issues on the localization problem. In addition, since the networks are evolving into more complex networks nowadays, more sophisticated supervised machine learning methods based on neural networks (NN) are proposed [18,21,28], such as ANN (artificial NN) [18], CNN (Convolutional NN) [28,38,39], DNN (deep NN) [21,28,29], RNN (recurrent NN) [38,40,41]. Most supervised machine learning methods concentrated on the training phase, which analyzes the mapping relationship between input and output layer through an unknown hidden layers' set.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Recent Indoor Localization Scenarios and Methodologies

Tian

Cabani

Chafouk

2021

Sensors

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Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

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Deep Learning Based Data Recovery for Localization

Cited by 24 publications

References 41 publications

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

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

Comparison of Wireless Adapters Interference Based on Differences of Floor Position

A Survey of Recent Indoor Localization Scenarios and Methodologies

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