Authors:
Aigerim Mussina
and
Sanzhar Aubakirov
Affiliation:
Department of Computer Science, al-Farabi Kazakh National University, Almaty and Kazakhstan
Keyword(s):
Bluetooth Low Energy, Indoor Positioning, RSSI, Machine Learning, Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Computational Intelligence
;
Data Engineering
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
The problem of real time location system is of current interest. Cities are growing up and buildings become more complex and large. In this paper we will describe the indoor positioning issue on the example of user tracking, while using the Bluetooth Low Energy technology and received signal strength indicator(RSSI). We experimented and compared our simple hand-crafted rules with the following machine learning algorithms: Naive Bayes and Support Vector Machine. The goal was to identify actual position of active label among three possible statuses and achieve maximum accuracy. Finally, we achieved accuracy of 0.95.