  Considered as a data-driven approach, Fingerprinting Localization Solutions
(FPSs) enjoy huge popularity due to their good performance and minimal
environment information requirement. This papers addresses applications of
artificial intelligence to solve two problems in Received Signal Strength
Indicator (RSSI) based FPS, first the cumbersome training database construction
and second the extrapolation of fingerprinting algorithm for similar buildings
with slight environmental changes. After a concise overview of deep learning
design techniques, two main techniques widely used in deep learning are
exploited for the above mentioned issues namely data augmentation and transfer
learning. We train a multi-layer neural network that learns the mapping from
the observations to the locations. A data augmentation method is proposed to
increase the training database size based on the structure of RSSI measurements
and hence reducing effectively the amount of training data. Then it is shown
experimentally how a model trained for a particular building can be transferred
to a similar one by fine tuning with significantly smaller training numbers.
The paper implicitly discusses the new guidelines to consider about deep
learning designs when they are employed in a new application context.
