scholarly journals A Simple Indoor Localization Methodology for Fast Building Classification Models Based on Fingerprints

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 103 ◽  
Author(s):  
David Sánchez-Rodríguez ◽  
Itziar Alonso-González ◽  
Carlos Ley-Bosch ◽  
Miguel Quintana-Suárez
Keyword(s):  
Indoor Localization ◽  
Estimation Error ◽  
Computational Cost ◽  
Access Point ◽  
Visible Light Communication ◽  
High Energy ◽  
Location Estimation ◽  
Localization System ◽  
Emergency Situations ◽  
Computational Performance

Indoor localization has received tremendous attention in the last two decades due to location-aware services being highly demanded. Wireless networks have been suggested to solve this problem in many research works, and efficient algorithms have been developed with precise location and high accuracy. Nevertheless, those approaches often have high computational and high energy consumption. Hence, in temporary environments, such as emergency situations, where a fast deployment of an indoor localization system is required, those methods are not appropriate. In this manuscript, a methodology for fast building of an indoor localization system is proposed. For that purpose, a reduction of the data dimensionality is achieved by applying data fusion and feature transformation, which allow us to reduce the computational cost of the classifier training phase. In order to validate the methodology, three different datasets were used: two of them are public datasets based mainly on Received Signal Strength (RSS) from different Wi-Fi access point, and the third is a set of RSS values gathered from the LED lamps in a Visible Light Communication (VLC) network. The simulation results show that the proposed methodology considerably amends the overall computational performance and provides an acceptable location estimation error.

scholarly journals An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

2020 ◽  
Vol 9 (4) ◽  
pp. 261
Author(s):  
Fan Xu ◽  
Xuke Hu ◽  
Shuaiwei Luo ◽  
Jianga Shang
Keyword(s):  
Indoor Localization ◽  
Attenuation Factor ◽  
Access Point ◽  
Location Estimation ◽  
Signal Attenuation ◽  
Distance Ratio ◽  
Localization Accuracy ◽  
Access Points ◽  
Attenuation Parameter ◽  
The Impact

Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

scholarly journals Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

2020 ◽  
Vol 12 (12) ◽  
pp. 1995
Author(s):  
David Sánchez-Rodríguez ◽  
Miguel A. Quintana-Suárez ◽  
Itziar Alonso-González ◽  
Carlos Ley-Bosch ◽  
Javier J. Sánchez-Medina
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Channel State ◽  
State Information ◽  
Single Access ◽  
5 Ghz

In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.

scholarly journals IMPROVED INDOOR LOCALIZATION WITH DIVERSITY AND FILTERING BASED ON RECEIVED SIGNAL STRENGTH MEASUREMENTS

2010 ◽  
pp. 9-15
Author(s):  
Andreas Fink ◽  
Helmut Beikirch ◽  
Matthias Voss
Keyword(s):  
Data Transmission ◽  
Indoor Localization ◽  
Estimation Error ◽  
Path Loss ◽  
Distance Estimation ◽  
Signal Strength ◽  
Location Estimation ◽  
Indoor Environments ◽  
Redundant Data ◽  
Unknown Node

Distance estimation by the evaluation of RSSI measurements is a simple and well-known technique to predict the position of an unknown node. Therefore the infrastructure does not have to be extended by expensive hardware for synchronization or direction approximation. However, with the localization based on RSSI measurements common and proven systems can be used for the infrastructure. For indoor environments the distance-pending path loss is affected by strong variations, especially appearing as frequency specific signal dropouts. A diversity concept with redundant data transmission in different frequency bands can reduce the dropout probability. If also space diversity and plausibility filtering are used, the Location Estimation Error can be reduced significantly. The investigations show that a good performance for precision and availability can also be reached with low infrastructural costs.

scholarly journals PERFORMANCE ANALYSIS OF CLASSIFICATION METHODS FOR INDOOR LOCALIZATION IN VLC NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 385-391 ◽  
Author(s):  
D. Sánchez-Rodríguez ◽  
I. Alonso-González ◽  
J. Sánchez-Medina ◽  
C. Ley-Bosch ◽  
L. Díaz-Vilariño
Keyword(s):  
Performance Analysis ◽  
Electromagnetic Interference ◽  
Indoor Localization ◽  
Average Distance ◽  
New Technology ◽  
Computational Cost ◽  
Visible Light Communication ◽  
Average Error ◽  
Services Research ◽  
Machine Leaning

Indoor localization has gained considerable attention over the past decade because of the emergence of numerous location-aware services. Research works have been proposed on solving this problem by using wireless networks. Nevertheless, there is still much room for improvement in the quality of the proposed classification models. In the last years, the emergence of Visible Light Communication (VLC) brings a brand new approach to high quality indoor positioning. Among its advantages, this new technology is immune to electromagnetic interference and has the advantage of having a smaller variance of received signal power compared to RF based technologies. In this paper, a performance analysis of seventeen machine leaning classifiers for indoor localization in VLC networks is carried out. The analysis is accomplished in terms of accuracy, average distance error, computational cost, training size, precision and recall measurements. Results show that most of classifiers harvest an accuracy above 90 %. The best tested classifier yielded a 99.0 % accuracy, with an average error distance of 0.3 centimetres.

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