scholarly journals CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

2018 ◽  
Vol 12 (1) ◽  
pp. 61 ◽  
Author(s):  
Yohanes Erwin Dari ◽  
Suyoto Suyoto Suyoto ◽  
Pranowo Pranowo Pranowo
Keyword(s):  
Nearest Neighbor ◽  
Access Point ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Positioning System ◽  
K Nearest Neighbor ◽  
Indoor Location ◽  
Pointing Device ◽  
Accurate Position ◽  
Indoor Positioning System

The existence of mobile devices as a location pointing device using Global Positioning System (GPS) is a very common thing nowadays. The use of GPS as a tool to determine the location of course has a shortage when used indoors. Therefore, the used of indoor location-based services in a room that leverages the use of Access Point (AP) is very important. By using the information of the Received Signal Strength (RSS) obtained from AP, then the location of the device can be determined without the need to use GPS. This technique is called the location fingerprint technique using the characteristics of received RSS’s fingerprint, then use it to determine the position. To get a more accurate position then authors used the K-Nearest Neighbor (KNN) method. KNN will use some of the data that obtained from some AP to assist in positioning the device. This solution of course would be able to determine the position of the devices in a storied building.

Amp-Phi: A CSI-Based Indoor Positioning System

2018 ◽  
Vol 32 (09) ◽  
pp. 1858005 ◽  
Author(s):  
Tao-Yun Zhou ◽  
Bao-Wang Lian ◽  
Yi Zhang ◽  
Sen Liu
Keyword(s):  
Nearest Neighbor ◽  
Phase Error ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Positioning System ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Error Mitigation ◽  
Indoor Positioning System

With rapid growth in the demand of location-based services (LBS) in indoor environments, localizations based on fingerprinting have attracted significant interest due to their convenience. Until now, most such methods were based on received signal strength indicator (RSSI), which is vulnerable to non-line-of-sight (NLOS). In order to realize high-precision indoor positioning, we propose a channel state information (CSI)-based Amp-Phi indoor-positioning system which exploits the amplitude and phase information of CSI at the same time to establish a fingerprinting database. Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build an NLOS mitigation model and a phase error mitigation model, respectively, to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher than that of PhaseFi, Horus and machine learning (ML) systems under the same condition. It can be used in many scenarios, such as the localization of robots in our daily life, by doctors or patients in the hospital, for people localization in large supermarkets or museums and so on.

scholarly journals Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 639 ◽  
Author(s):  
Osamah Abdullah
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Healthcare Providers ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Positioning System ◽  
K Nearest Neighbor ◽  
Multivariate Distributions ◽  
Major Drawback ◽  
Indoor Positioning System

Modern indoor positioning system services are important technologies that play vital roles in modern life, providing many services such as recruiting emergency healthcare providers and for security purposes. Several large companies, such as Microsoft, Apple, Nokia, and Google, have researched location-based services. Wireless indoor localization is key for pervasive computing applications and network optimization. Different approaches have been developed for this technique using WiFi signals. WiFi fingerprinting-based indoor localization has been widely used due to its simplicity, and algorithms that fingerprint WiFi signals at separate locations can achieve accuracy within a few meters. However, a major drawback of WiFi fingerprinting is the variance in received signal strength (RSS), as it fluctuates with time and changing environment. As the signal changes, so does the fingerprint database, which can change the distribution of the RSS (multimodal distribution). Thus, in this paper, we propose that symmetrical Hölder divergence, which is a statistical model of entropy that encapsulates both the skew Bhattacharyya divergence and Cauchy–Schwarz divergence that are closed-form formulas that can be used to measure the statistical dissimilarities between the same exponential family for the signals that have multivariate distributions. The Hölder divergence is asymmetric, so we used both left-sided and right-sided data so the centroid can be symmetrized to obtain the minimizer of the proposed algorithm. The experimental results showed that the symmetrized Hölder divergence consistently outperformed the traditional k nearest neighbor and probability neural network. In addition, with the proposed algorithm, the position error accuracy was about 1 m in buildings.

scholarly journals A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sajida Imran ◽  
Young-Bae Ko
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Positioning System ◽  
Local Fisher Discriminant Analysis ◽  
Indoor Positioning System

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

scholarly journals A REVIEW ON INDOOR LOCALIZATION WITH INTERNET OF THINGS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 121-128
Author(s):  
C. Basri ◽  
A. Elkhadimi
Keyword(s):  
Internet Of Things ◽  
Network Architecture ◽  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Location Based Services ◽  
Positioning System ◽  
Resources Management ◽  
Indoor Positioning System

Abstract. The advancement of Internet of things (IoT) has revolutionized the field of telecommunication opening the door for interesting applications such as smart cities, resources management, logistics and transportation, wearables and connected healthcare. The emergence of IoT in multiple sectors has enabled the requirement for an accurate real time location information. Location-based services are actually, due to development of networks, sensors, wireless communications and machine learning algorithms, able to collect and transmit data in order to determine the target positions, and support the needs imposed by several applications and use cases. The performance of an indoor positioning system in IoT networks depends on the technical implementation, network architecture, the deployed technology, techniques and algorithms of positioning. This paper highlights the importance of indoor localization in internet of things applications, gives a comprehensive review of indoor positioning techniques and methods implemented in IoT networks, and provides a detailed analysis on recent advances in this field.

WiFi and Bluetooth Low Energy Beacons for Indoor Positioning System

2019 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Nurul Fatehah Zulkpli ◽  
Nor Azlina Ab. Aziz ◽  
Noor Ziela Abd Rahman ◽  
Rosli Besar
Keyword(s):  
Indoor Positioning ◽  
Low Energy ◽  
Location Based Services ◽  
Positioning System ◽  
Indoor Environments ◽  
Wireless Technologies ◽  
Bluetooth Low Energy ◽  
Advantages And Disadvantages ◽  
Performance Accuracy ◽  
Indoor Positioning System

Indoor Positioning System (IPS) is used to locate a person, an object or a location inside a building. IPS is important in providing location-based services, which has recently gain much popularity. The services ease visitors’ navigation at unfamiliar premises. Location-based services depend on the capability of IPS to accurately determine the location of the user, which is a challenging issue in indoor environments. Several wireless technologies are available. In this paper, two of the most widely used IPS technologies are reviewed which are, WiFi and Bluetooth low energy (BLE). Their advantages and disadvantages are reviewed and reported here. Comparison of the systems based on their performance, accuracy and limitations are presented as well.

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