A Comprehensive Study of Bluetooth Fingerprinting-Based Algorithms for Localization

Abstract

There is an increasing demand for indoor navigation and localization systems along with the increasing popularity of location based services in recent years. According to past researches, Bluetooth is a promising technology for indoor wireless positioning due to its cost-effectiveness and easy-to-deploy feature. This paper studied three typical fingerprinting-based positioning algorithms -kNN, Neural Networks and SVM. According to our analysis and experimental results, the kNN regression method is proven to be a good candidate for localization in real-life application. Comprehensive performance comparisons including accuracy, precision and training time are presented.

An Analysis of Multiple Criteria and Setups for Bluetooth Smartphone-Based Indoor Localization Mechanism

“…In order to reduce the mean positioning error, different classification algorithms have been studied as new localization techniques based on fingerprinting. However, one of the main challenges is to properly tune the various parameters of the classification algorithms, since they play a major role in the achievable accuracy and mean positioning error [29,30]. In [29], the authors have compared three different classification algorithms, Neural Networks, SVM, and -NN.…”

Positioning in Bluetooth and Uwb Networks

“…The proposed solutions for Bluetooth localization rely mostly on RSS-based MLAT (Wamg et al, 2013;Hallberg et al, 2003;Cinefra, 2013;Fernandez et al, 2007;Chen et al, 2014), fingerprinting (Bandara et al,2004;Zhang et al, 2013;Disha and Khilary, 2013), or on a combination of both (Subhan et al, 2011;Subhan et al, 2013) 9 . In Bluetooth positioning, unlike in WiFi networks, there are many MLAT based location solutions.…”

Indoor Localization based on Bluetooth Technology: A Brief Review

“…In online phase, the position is estimated by matching the RSS of the mobile node with that of RSS of the closest lying reference point in the RM. In [3], three fingerprinting based algorithms are compared. Two metrices are taken into consideration: accuracy and precision.…”

“…Indoor localization refers to finding the location or position of any mobile device, node or any object having a wireless link to some known points [1].Locations such as hospitals, airports, shopping malls, national libraries sometimes become confusing and complex to travel and users travelling are benefited by guidance provided by indoor localization [2].Extreme environments such as fire scenes, underground mines are installed with navigation systems to increase the chance of survival [3].Also, intelligent environments can be created by having knowledge of people's presence, number of people, movement and their behavior [4]. GPS uses satellites to find out the position and is very accurate.…”