A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems

2021 ◽  
Vol 101 (3) ◽  
Author(s):  
Priya Roy ◽  
Chandreyee Chowdhury
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Machine Learning Techniques ◽  
Navigation Systems ◽  
Learning Techniques

QARTA

2021 ◽  
Vol 14 (11) ◽  
pp. 2273-2282
Author(s):  
Mashaal Musleh ◽  
Sofiane Abbar ◽  
Rade Stanojevic ◽  
Mohamed Mokbel
Keyword(s):  
Machine Learning ◽  
Road Network ◽  
Contextual Information ◽  
Time Of Day ◽  
Food Delivery ◽  
Machine Learning Techniques ◽  
Navigation Systems ◽  
Query Result ◽  
Learning Techniques ◽  
Map Service

Maps services are ubiquitous in widely used applications including navigation systems, ride sharing, and items/food delivery. Though there are plenty of efforts to support such services through designing more efficient algorithms, we believe that efficiency is no longer a bottleneck to these services. Instead, it is the accuracy of the underlying road network and query result. This paper presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to construct its own highly accurate map, not only in terms of map topology but more importantly, in terms of edge weights. QARTA also employs machine learning techniques to calibrate its query answers based on contextual information, including transportation modality, location, and time of day/week. QARTA is currently deployed in all Taxis and the third largest food delivery company in the State of Qatar, replacing the commercial map service that was in use, and responding in real-time to hundreds of thousands of daily API calls. Experimental evaluation of QARTA shows its comparable or higher accuracy than commercial services.

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

scholarly journals INDOOR LOCALIZATION FOR 3D MOBILE CADASTRAL MAPPING USING MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol VI-4/W1-2020 ◽  
pp. 159-166
Author(s):  
C. Potsiou ◽  
N. Doulamis ◽  
N. Bakalos ◽  
M. Gkeli ◽  
C. Ioannidis
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Indoor Localization ◽  
Machine Learning Techniques ◽  
Technical Solution ◽  
Data Infrastructure ◽  
Learning Techniques ◽  
Data Capturing ◽  
Floor Plans ◽  
Fit For Purpose

Abstract. With the rapid global urbanization, several multi-dimensional complex infrastructures have emerged, introducing new challenges in the management of the vertically stratified buildings spaces. 3D indoor cadastral spaces consist a zestful research topic as their complexity and geometry alterations during time, prevents the assignment of the corresponding Rights, Restrictions and Responsibilities (RRR). In the absence of the necessary horizontal spatial data infrastructure/floor plans their determination is weak. In this paper a fit-for-purpose technical framework and a crowdsourced methodology for the implementation of 3D cadastral surveys focused on indoor cadastral spaces, is proposed and presented. As indoor data capturing tool, an open-sourced cadastral mobile application for Android devices, is selected and presented. An Indoor Positioning System based on Bluetooth technology is established while an innovative machine learning architecture is developed, in order to explore its potentials to automatically provide the position of the mobile device within an indoor environment, aiming to add more intelligence to the proposed 3D crowdsourced cadastral framework. A practical experiment for testing the examined technical solution is conducted. The produced results are assessed to be quite promising.

scholarly journals Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison

2021 ◽  
Vol 309 ◽  
pp. 01024
Author(s):  
M. Sri Vidya ◽  
G. R. Sakthidharan
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Global Positioning ◽  
Iot Devices

Internet of Things connects various physical objects and form a network to do the services for sensing the physical things without any human intervention. They compute the data, retrieve the data by the network connections made through IoT device components such as Sensors, Protocols, Address, etc., The Global Positioning System (GPS) is used for localization in outer areas such as roads, and ground but cannot be used for Indoor environment. So, while using Indoor Environment, finding or locating an object is not possible by GPS. Therefore by using IoT devices such as Wi-Fi routers in Indoor Environment can localize the objects. It can be done by using Received Signal Strengths (RSSs) from a Wi-Fi router. But by using RSSs in Wi-Fi, there are disturbances, reflections, interferences are caused. By using Outlier detection techniques for localization can identify the objects clearly without any interruptions, noises, and irregular signal strengths. This paper produces research about Indoor Situating Environment and various techniques already used for localization and form the effective solution. The several methods used are compared and form a result to make the further computation in the Indoor Environment. The Comparison is done in order to find the effective and more accurate Machine Learning algorithms used for Indoor Localization.

scholarly journals Machine Learning Based Indoor Localisation Using Wi-Fi And Smartphone

10.31645/06 ◽  
2020 ◽  
Author(s):  
Zulqarnain Khokhar ◽  
◽  
Murtaza Ahmed Siddiqi ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Indoor Localization ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Gradient Boosting ◽  
Learning Techniques ◽  
Indoor Localisation

Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machine-learning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.

scholarly journals Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1678 ◽  
Author(s):  
Ahmed H. Salamah ◽  
Mohamed Tamazin ◽  
Maha A. Sharkas ◽  
Mohamed Khedr ◽  
Mohamed Mahmoud
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Indoor Localization ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Indoor Environments ◽  
Learning Approaches ◽  
Positioning Systems ◽  
Learning Techniques ◽  
And Performance

The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m2 to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Sign in / Sign up

Export Citation Format

Share Document