… In this paper, we proposed a deep learning-based RFFI scheme to classify LoRa devices
and carried out extensive experimental evaluations involving 25 LoRa devices from five …

… 3 DEEP LEARNING AND DATA AUGMENTATION METHODOLOGY We considered two
popular deep learning approaches with distinct architectures; a CNN and an RNN. The intention …

… robustness of deep-learningbased RF fingerprinting of LoRa-… by hardware impairments to
provide device-specific signatures … Using LoRa RF datasets, we show that the deep learning …

… The dataset consists of 123,529 LoRaWAN messages received at 68 base-stations,
which are the gateways to transfer data from LoRa devices to the application layer. The …

… Tensorflow is an open-source, deep-learning library used for machine learning applications…
gained from deep learning and an IoT, which integrates millions of smart devices together, …

… LoRa network through a deep learning-based approach. Two strategies are proposed:
regression for bit detection based on a deep … In LoRa, the devices are usually located far from the …

… the use of LoRa device fingerprinting RSS data in indoor localization using the deep
learning method. Some of these contributions are as follows: 1. Using LoRa technology as an …

… Hwang, and JK Choi, “A novel deep learning-based IoT device transmission interval
management scheme for enhanced scalability in LoRa networks,” IEEE Wireless Commun. Lett., vol…

… mostly sensors and tracking devices. (ii) … Deep learning models, the first one for pollution
predictions based on a meteorological dataset found on the Internet, it’sa simple Deep learning …

… Finally, we conclude this paper by discussing the existing challenges in deep learning-based
LoRa device identification and also envisage future research directions and opportunities. …