An enhanced WiFi indoor localization system based on machine learning

Salamah, Ahmed H.; Tamazin, Mohamed; Sharkas, Maha; Khedr, Mohamed

doi:10.1109/ipin.2016.7743586

Cited by 114 publications

(82 citation statements)

References 13 publications

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“…[22][23][24][25] In this study decision trees, principal component analysis (PCA), linear discriminant analysis (LDA), nearest neighbor algorithms, and autoencoders (AEs) are used and their performances are analyzed in the classification phase. [22][23][24][25] In this study decision trees, principal component analysis (PCA), linear discriminant analysis (LDA), nearest neighbor algorithms, and autoencoders (AEs) are used and their performances are analyzed in the classification phase.…”

Section: Machine Learning Techniques For Classification Phasementioning

confidence: 99%

Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment

Turgut

Üstebay

Aydın

et al. 2019

Trans Emerging Tel Tech

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The ability to detect the mobile user's location with high precision in indoor networks is particularly difficult due to the environmental characteristics and high dynamics of the indoor networks. The use of different technologies in the system to be developed to determine the position with high accuracy is important for overcoming the disadvantage(s) of any technology. To design a high‐precision indoor positioning method, it is important create an Internet of things (IoT) environment which uses hybrid technologies. The proposed system has been tested on an IoT environment by using two phases: preproccessing and localization. The corresponding environment is an original IoT environment, which allows to collect a hybrid dataset consists of WiFi, Bluetooth Low Energy, and Earth's magnetic field values. HALICDB dataset is created in the related ecosystem. To make a comparison with HALICDB, RFKONDB is used to create RFKON_HIBRID with similar signal values. The signal values obtained from the datasets are first passed through a particle filter. In the localization phase, a reference signal map is obtained for the detection of the moving objects in the indoor areas that are in the IoT environment using the fingerprint method. In the offline phase, the different machine learning methods are used in fingerprint maps for classification. It is seen that the highest accuracy is received through stacked sparse autoencoder from the deep learning methods due to the overcomplete network structure offered by the IoT environment on both datasets.

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Section: Machine Learning Techniques For Classification Phasementioning

confidence: 99%

Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment

Turgut

Üstebay

Aydın

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…This process continues until all the tree nodes reach the similar output targets. The Random Forest classifier takes weights based on the input as a parameter that resembles the number of the decision trees [18]. …”

Section: Naïve Bayesmentioning

confidence: 99%

“…However, RADAR system was the first introducing the fingerprinting technique based on the deterministic approach [13] using -Nearest Neighbor method (KNN) [14]. Nowadays, the use of machine learning algorithms (ML) as in [15] has increasingly gained more popularity in indoor navigation domain because of their witnessed robustness; among them are Naïve Bayes classifier [16,17], Support Vector Machine (SVM) [17,18], Random Forest [18,19], and Neural Network [20,21]. However, their model generalization to different user's terminals has seldom been considered.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

Rezgui

Pei

Chen

et al. 2017

Mobile Information Systems

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This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI) of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM). Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF), Signal Strength Difference (SSD), and the Hyperbolic Location Fingerprinting (HLF) based SVM. The results show that the NR-SVM outperforms these popular methods.

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“…For example, Leins and Steiner and Taok et al used power at different UWB frequencies for features, and Dayekh et al used power at WiFi frequencies for features. Zhou and Wieser and Salamah et al also used WiFi signals but opted to use received signal strength (RSS) as features. Ergut also used RSS but with cellular signals.…”

Section: Introductionmentioning

confidence: 99%

“…The features developed for fingerprints vary on the application. For example, Leins 37 also used WiFi signals but opted to use received signal strength (RSS) as features. Ergut 40 also used RSS but with cellular signals.…”

Section: Introductionmentioning

confidence: 99%

Navigation using VLF signals with artificial neural networks

2018

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This examines the use of very low frequency (VLF) electromagnetic signals with artificial neural networks (ANN) to estimate two‐dimensional location. The VLF source positions and type of signals are not known in advance. VLF signals were collected from the environment using a mobile antenna while the user manually tagged the true location. Data were collected over several months. The signals were divided into time segments, and features were generated for the ANN from the spectral power of the signals over time. The ANN uses long short‐term memory (LSTM) layers and fully connected layers and was trained using the time‐series spectral features of the VLF signal and the true location data. The position estimates are an indoor locale‐classification problem. Our task was to determine which of 50 rooms or hallway segments the sensor was in for a given building. The system achieved an average accuracy over 76% in determining discrete rooms or hallways. The approach achieved these exceptional accuracies with very little a priori knowledge of the VLF signals using only supervised learning.

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An enhanced WiFi indoor localization system based on machine learning

Cited by 114 publications

References 13 publications

Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment

Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

Navigation using VLF signals with artificial neural networks

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