Publication Date:
2019-11-14
Description:
Development of indoor location systems that use smartphone sensors has been a topic of interest to industry and academia. In this paper, we describe an experiment that was performed to evaluate the feasibility of creating a mobile indoor localization model based on data from participatory sensing. To achieve it, seven smartphone users used their integrated magnetometers to collected magnetic field information on a building. The data collected are utilized to train three machine learning algorithms: The k Nearest Neighbors (KNN), Decision Trees (J48), and Naïve Bayes algorithms. The performance of the algorithms was measured through the accuracy and kappa statistics. Our results indicate that it is possible to create an infrastructure-less indoor localization model at room level using data from participatory sensing. The model with the most significant performance was obtained with the KNN, since it offers an accuracy of 97.12%, while the model with the most reduced performance was Naïve Bayes, since it offers an accuracy of 50.79%.
Electronic ISSN:
2504-3900
Topics:
Technology
