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Projecte llegit

Títol: Applying Machine Learning Approaches to Indoor Positioning


Estudiants que han llegit aquest projecte:


Director/a: ZOLA, ENRICA VALERIA

Departament: ENTEL

Títol: Applying Machine Learning Approaches to Indoor Positioning

Data inici oferta: 17-07-2020     Data finalització oferta: 17-03-2021



Estudis d'assignació del projecte:
    MU MASTEAM 2015
Tipus: Individual
 
Lloc de realització: EETAC
 
Segon director/a (UPC): MARTIN ESCALONA, ISRAEL
 
Paraules clau:
802.11, indoor positioning, fingerprinting, machine learning
 
Descripció del contingut i pla d'activitats:
The aim of the thesis is to develop a predictive routine that
is able to provide the position of 802.11 devices in indoor
scenarios. It consists of two parts: on the one hand, the
student has to perform the measurements and get the
fingerprinting map of the building. The data will be
automatically uploaded to a local server, from which they will
be available for further handling during the prediction phase.
The second part of the project consists of analysing different
machine learning algorithms and propose a valid solution that
is able to predict the position of a given user by matching its
measurements with those stored in the fingerprinting map.
 
Overview (resum en anglès):
For commercial demands, common use cases such as shopping in malls, or supermarkets, warehouse management, game development and so on, are continuously improved. This is where positioning systems, or more specifically indoor positioning systems play an important role.
For outdoor environmentws the Global Positioning System (GPS) is nowadays the most relyable technology, but indoor location and navigation remain unresolved issues, and alternative methods are needed. In this scenario the Wi-Fi positioning system is a pretty widely used technology, indeed since large buildings mostly already have a high number of Wi-Fi routers distributed, it makes sense to use the Wi-Fi signals for positioning. Namely, it is possible to employ Received Signal Strength (RSS) signals from any 802.11 device, while Round-Trip Time (RTT) signals from any 802.11mc device.
This study investigates the implementation of a 802.11-based indoor positioning system that will follow a fingerprinting approach: the Wi-Fi signal collected at a given point will be compared with a map of measurements previously recorded at several reference points and, by means of machine learning, the most probable location will be extracted from the database.
The objective will be to study the real potential of a Wi-Fi based indoor positioning system that simultaneously exploits RTT and RSS information from IEEE 802.11 devices.
In order to build the required database, a data collection campaign has been implemented in one of the buildings of Universitat Politècnica de Catalunya. Subsequently, two machine learning algorithms have been implemented using Python¿s scikit-learn and xgboost libraries, and the related predictive performances have been tested. The positioning errors referring to the use of different datasets (e.g., RTT dataset, RSS dataset, the combined RTT/RSS dataset) have been estimated. Next, the results related to positional errors will be outlined, together with the relative benefits, if any, of using a coupled dataset for both considered classifiers.


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