scholarly journals Improving Accuracy and Simplifying Training in Fingerprinting-Based Indoor Location Algorithms at Room Level

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Mario Muñoz-Organero ◽  
Claudia Brito-Pacheco
Keyword(s):  
Artificial Intelligence ◽  
Radio Wave ◽  
Mobile Devices ◽  
Signal Strength ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Indoor Location ◽  
Improving Accuracy ◽  
Indoor Location Systems ◽  
Decision Algorithms

Fingerprinting-based algorithms are popular in indoor location systems based on mobile devices. Comparing the RSSI (Received Signal Strength Indicator) from different radio wave transmitters, such as Wi-Fi access points, with prerecorded fingerprints from located points (using different artificial intelligence algorithms), fingerprinting-based systems can locate unknown points with a few meters resolution. However, training the system with already located fingerprints tends to be an expensive task both in time and in resources, especially if large areas are to be considered. Moreover, the decision algorithms tend to be of high memory and CPU consuming in such cases and so does the required time for obtaining the estimated location for a new fingerprint. In this paper, we study, propose, and validate a way to select the locations for the training fingerprints which reduces the amount of required points while improving the accuracy of the algorithms when locating points at room level resolution. We present a comparison of different artificial intelligence decision algorithms and select those with better results. We do a comparison with other systems in the literature and draw conclusions about the improvements obtained in our proposal. Moreover, some techniques such as filtering nonstable access points for improving accuracy are introduced, studied, and validated.

RADIO PROPAGATION CHARACTERISTICS OF INDOOR LOCATION SYSTEM BASED ON RSSI AT 490 MHz

2014 ◽  
Vol 23 (07) ◽  
pp. 1450094 ◽  
Author(s):  
WEIHONG FAN ◽  
MAJID AHMADI ◽  
FENG XUE
Keyword(s):  
Indoor Environment ◽  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Propagation Characteristic ◽  
Radio Propagation ◽  
Test Results ◽  
Received Signal Strength Indicator ◽  
Indoor Location ◽  
Localization And Tracking

Localization and tracking technology based on received signal strength indicator (RSSI) is one of the most popular topics because of its low demand on hardware and cost. But the complexity of the indoor environment, leads to the uncertainty of the radio propagation which can seriously affect the positioning accuracy based on the received signal strength. Focused on the wall reflection in the indoor environment, the radio propagation characteristic based on ray-tracing model is analyzed and one strategy for the near wall localization is presented. The actual hardware platform and experimental test results show the applicability of the empirical logarithmic path loss model for localization and the effect of the wall reflection.

scholarly journals Indoor localization system based on virtual access points with filtering schemes

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986613 ◽  
Author(s):  
Dong Myung Lee ◽  
Boney Labinghisa
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Performance Testing ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Indoor Environments ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Error Distance

In indoor positioning techniques, Wi-Fi is one of the most used technology because of its availability and cost-effectiveness. Access points are usually the main source of Wi-Fi signals in an indoor environment. If access points are optimized to cover the indoor area, this could improve Wi-Fi signal distribution. This article proposed an alternative to optimizing access point placement and distribution by introducing virtual access points that can be virtually placed in any part of the indoor environment without installation of actual access points. Virtual access points will be created heuristically by correlating received signal strength indicator of already existing access points and through linear regression. After introducing virtual access points in the indoor environment, next will be the addition of filters to improve signal fluctuation and reduce noise interference. Kalman filter has been previously used together with virtual access point and showed improvement by decreasing error distance of Wi-Fi fingerprinting results. This article also aims to include particle filter in the system to further improve localization and test its effectiveness when paired with Kalman filter. The performance testing of the algorithm in different indoor environments resulted in 3.18 and 3.59 m error distances. An improvement was added on the system by using relative distances instead of received signal strength indicator values in distance estimation and gave an error distance average of 1.85 m.

scholarly journals Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1350 ◽  
Author(s):  
Sharareh Naghdi ◽  
Kyle O’Keefe
Keyword(s):  
Artificial Intelligence ◽  
Human Body ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Low Energy ◽  
Wireless Technology ◽  
Received Signal Strength Indicator ◽  
Bluetooth Low Energy ◽  
Office Environment

One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

scholarly journals An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system

2020 ◽  
Vol 10 (1) ◽  
pp. 117-123
Author(s):  
Bhulakshmi Bonthu ◽  
M Subaji
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning ◽  
Positioning System ◽  
Radio Waves ◽  
Direct Path ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Measuring Techniques ◽  
Indoor Positioning System

AbstractIndoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy.

scholarly journals UJAmI Location: A Fuzzy Indoor Location System for the Elderly

2021 ◽  
Vol 18 (16) ◽  
pp. 8326
Author(s):  
Antonio-Pedro Albín-Rodríguez ◽  
Yolanda-María De-La-Fuente-Robles ◽  
José-Luis López-Ruiz ◽  
Ángeles Verdejo-Espinosa ◽  
Macarena Espinilla Estévez
Keyword(s):  
Fuzzy Logic ◽  
Elderly People ◽  
Mobile Devices ◽  
The Elderly ◽  
Indoor Environments ◽  
Radio Waves ◽  
Indoor Location ◽  
Location System ◽  
Fuzzy Logic Approach ◽  
Indoor Location Systems

Due to the large number of elderly people with physical and cognitive issues, there is a strong need to provide indoor location systems that help caregivers monitor as many people as possible and with the best quality possible. In this paper, a fuzzy indoor location methodology is proposed in a smart environment based on mobile devices and Bluetooth Low Energy (BLE) beacons where a set of Received Signal Strength Indicators (RSSI) is received by mobile devices worn by the inhabitants. The use of fuzzy logic and a fuzzy linguistic approach is proposed to deal with the imprecise nature of the RSSI values, which are influenced by external factors such as radio waves, causing significant fluctuations. A case study carried out at the Smart Lab of the University of Jaén (UJAmI Smart Lab) is presented to demonstrate the effectiveness of the proposed methodology, where our proposal is compared with a non-fuzzy logic approach, obtaining an accuracy of 91.63%, approximately 10 points higher than the methodology without using fuzzy logic. Finally, our theoretical proposal is accompanied by a description of the UJAmI Location system, which applies the theory to the functionality of locating elderly people in indoor environments.

scholarly journals Comparison of CNN Applications for RSSI-based Fingerprint Indoor Localization

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 989 ◽  
Author(s):  
Sinha ◽  
Hwang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Indoor Localization ◽  
Signal Strength ◽  
Test Accuracy ◽  
Computational Power ◽  
Location Error ◽  
Received Signal Strength Indicator

The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning.

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