Indoor Localization through Mobile Participatory Sensing and Magnetic Field

Developing location systems that use mobile device sensors has been a topic of interest to industry and the academy. In this paper, we describe an experiment that was performed for evaluating the feasibility to create a mobile indoor localization model at room level based on data from participatory sensing. In order to achieve it, seven subjects that had a smartphone with magnetometer collected magnetic field information in a building composed by five rooms with different dimensions. The information collected was used to train three machine learning algorithms: k Nearest Neighbors (kNN), decision trees (J48) and Naïve Bayes. The performance of the algorithms was measured through the accuracy and the kappa statistics. Our results show that it is possible to create a mobile indoor localization model at room level using data from participatory sensing. The model with the highest performance was obtained with the kNN algorithm with a value of k = 3, since this offer an accuracy of 97.12% with a concordance level (kappa) of 0.9639 to estimate the localization of individual inside a room at the indoor environment. While the model with the lowest performance was Naïve Bayes, it offers an accuracy of 50.79% with a concordance level of 0.3834.