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.

翻译：现有的大多数定位解决方案依赖于RSSI指纹定位技术。然而在LoRa网络中，由于网络设置中的扩频因子(SF)存在，传统指纹定位方法可能缺乏对无线电地图的代表性，导致定位估计不准确。为此，本研究提出一种考虑扩频因子的新型LoRa RSSI指纹定位方法。性能评估表明，与现有最先进方法相比，该方法可将定位精度提升高达6.67%。该评估基于作为基准的全连接深度神经网络(DNN)完成。为进一步提升定位精度，我们提出一种深度强化学习模型，该模型能够捕捉LoRa网络日益增长的复杂性并应对其可扩展性挑战。实验结果表明，与基准DNN模型相比，该模型的定位精度提升了48.10%。