  Most of the developed localization solutions rely on RSSI fingerprinting.
However, in the LoRa networks, due to the spreading factor (SF) in the network
setting, traditional fingerprinting may lack representativeness of the radio
map, leading to inaccurate position estimates. As such, in this work, we
propose a novel LoRa RSSI fingerprinting approach that takes into account the
SF. The performance evaluation shows the prominence of our proposed approach
since we achieved an improvement in localization accuracy by up to 6.67%
compared to the state-of-the-art methods. The evaluation has been done using a
fully connected deep neural network (DNN) set as the baseline. To further
improve the localization accuracy, we propose a deep reinforcement learning
model that captures the ever-growing complexity of LoRa networks and copes with
their scalability. The obtained results show an improvement of 48.10% in the
localization accuracy compared to the baseline DNN model.
