Machine Learning Inspired Binocular Vision Indoor Positioning System Based on QR Code Beacon

2020 ◽  
Author(s):  
Xinyi Deng ◽  
Dazhong He ◽  
Wenli Zhou ◽  
Yang Yang ◽  
Yue Zhang
Keyword(s):  
Machine Learning ◽  
Binocular Vision ◽  
Indoor Positioning ◽  
Qr Code ◽  
Positioning System ◽  
Indoor Positioning System

A Multi-Criteria Decision Strategy to Select a Machine Learning Method for Indoor Positioning System

2018 ◽  
Vol 27 (05) ◽  
pp. 1850018 ◽  
Author(s):  
Ahmet Yazıcı ◽  
Sinem Bozkurt Keser ◽  
Serkan Günal ◽  
Uğur Yayan
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Learning Algorithms ◽  
Computation Time ◽  
Indoor Positioning ◽  
Support Vector ◽  
Positioning System ◽  
Decision Strategy ◽  
Positioning Systems ◽  
Indoor Positioning System

Indoor positioning system is an active research area. There are various performance metrics such as accuracy, computation time, precision, and f-score in machine learning based indoor positioning systems. The aim of this study is to present a multi-criteria decision strategy to determine suitable machine learning methods for a specific indoor positioning system. This helps to evaluate the performance of machine learning algorithms considering multiple criteria. During the experiments, UJIIndoorLoc, KIOS and RFKON datasets are used from the positioning literature. The algorithms such as k-nearest neighbor, support vector machine, decision tree, naïve bayes and bayesian networks are compared using these datasets. In addition to these, ensemble learning algorithms, namely adaboost and bagging, are utilized to improve the performance of these classifiers. As a conclusion, the test results for any specific dataset are reevaluated using the performance metrics such as accuracy, f-score and computation time, and a multi-criteria decision strategy is proposed to find the most convenient algorithm. The analytical hierarchy process is used for multi-criteria decision. To the best of our knowledge, this is the first work to select the proper machine learning algorithm for an indoor positioning system using multi-criteria decision strategy.

scholarly journals A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3766 ◽  
Author(s):  
Soumya Rana ◽  
Javier Prieto ◽  
Maitreyee Dey ◽  
Sandra Dudley ◽  
Juan Corchado
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Positioning System ◽  
Indoor Location ◽  
Indoor Scenarios ◽  
Indoor Location Systems ◽  
Indoor Positioning System

Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

scholarly journals Reduced Tilting Effect of Smartphone CMOS Image Sensor in Visible Light Indoor Positioning

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1635
Author(s):  
Md Habibur Rahman ◽  
Mohammad Abrar Shakil Sejan ◽  
Jong-Jin Kim ◽  
Wan-Young Chung
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Light Emitting Diode ◽  
Cmos Image Sensor ◽  
Indoor Positioning ◽  
Image Sensor ◽  
Image Features ◽  
Oxide Semiconductor ◽  
Positioning System ◽  
Indoor Positioning System

Visible light positioning (VLP) using complementary metal–oxide–semiconductor (CMOS) image sensors is a cost-effective solution to the increasing demand for an indoor positioning system. However, in most of the existing VLP systems with an image sensor, researchers assume that the receiving image sensor is positioned parallel to the indoor floor without any tilting and, thus, have only focused on the high-precision positioning algorithm and ignored the proper light-emitting diode (LED)-ID recognition. To address these limitations, we present, herein, a smartphone CMOS image sensor and visible light-based indoor localization system for a receiver device in a tilted position, and we have applied a machine learning approach for optimized LED-ID detection. For detection of the LED-ID, we generated different features for different LED-IDs and utilize a machine learning method to identify each ID as opposed to using the conventional coding and decoding method. An image processing method was used for the image features extraction and selection. We utilized the rolling shutter mechanism of the smartphone CMOS image sensor in our indoor positioning system. Additionally, to improve the LED-ID detection and positioning accuracy with the tilting of the receiver, we utilized the embedded fusion sensors of the smartphone (e.g., accelerometer, gyroscope, and magnetometer, which can be used to extract the yaw, pitch, and roll angles). The experimental results for the proposed positioning system show that it can provide 2.49, 4.63, 8.46, and 12.20 cm accuracy with angles of 0, 5, 10, and 15°, respectively, within a 2 m × 2 m × 2 m positioning area.

scholarly journals Analyzing Machine Learning Models with Gaussian Process for the Indoor Positioning System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yunxin Xie ◽  
Chenyang Zhu ◽  
Wei Jiang ◽  
Jia Bi ◽  
Zhengwei Zhu
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Learning Approaches ◽  
Positioning Accuracy ◽  
Access Points ◽  
Indoor Positioning System

Recently, there has been growing interest in improving the efficiency and accuracy of the Indoor Positioning System (IPS). The Received Signal Strength- (RSS-) based fingerprinting technique is essential for indoor localization. However, it is challenging to estimate the indoor position based on RSS’s measurement under the complex indoor environment. This paper evaluates three machine learning approaches and Gaussian Process (GP) regression with three different kernels to get the best indoor positioning model. The hyperparameter tuning technique is used to select the optimum parameter set for each model. Experiments are carried out with RSS data from seven access points (AP). Results show that GP with a rational quadratic kernel and eXtreme gradient tree boosting model has the best positioning accuracy compared to other models. In contrast, the eXtreme gradient tree boosting model could achieve higher positioning accuracy with smaller training size and fewer access points.

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