  Indoor localization has been a hot area of research over the past two
decades. Since its advent, it has been steadily utilizing the emerging
technologies to improve accuracy, and machine learning has been at the heart of
that. Machine learning has been increasingly used in fingerprint-based indoor
localization to replace or emulate the radio map that is used to predict
locations given a location signature. The prediction quality of a machine
learning model primarily depends on how well the model was trained, which
relies on the amount and quality of data used to train it. Data augmentation
has been used to improve quality of the trained models by synthetically
producing more training data, and several approaches were used in the
literature that tackles the problem of lack of training data from different
angles. In this paper, we propose DataLoc+, a data augmentation technique for
room-level indoor localization that combines different approaches in a simple
algorithm. We evaluate the technique by comparing it to the typical direct
snapshot approach using data collected from a field experiment conducted in a
hospital. Our evaluation shows that the model trained using the proposed
technique achieves higher accuracy. We also show that the technique adapts to
larger problems using a limited dataset while maintaining high accuracy.
