Abstract
LoRaWAN networks, which are extremely popular today, are based on the LoRa protocol and offer very long communication ranges, but they also come with significant limitations. These limitations stem primarily from two factors: duty cycle and maximum message size. During image transmission over LoRa-based networks, packet loss is a common problem resulting from limited bandwidth and transmission interference. During image transmission, it leads to missing data in the received content, most often visible as vertical or horizontal lines. We present a method to repair such corrupted images using a fully convolutional neural network inspired by the U-Net architecture. The experiments carried out show that the proposed approach effectively reconstructs missing parts of the image, achieving high structural similarity (SSIM) to the original. The proposed method can be applied to image transmission and reconstruction on low-power devices, typical of IoT systems that use LoRa for communication.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.76
Reconstruction of Images Transmitted over LoRa Networks Using Fully Convolutional Neural Network
LoRaWAN networks, which are extremely popular today, are based on the LoRa protocol and offer very long communication ranges, but they also come with significant limitations. These limitations stem primarily from two factors: duty cycle and maximum message size. During image transmission over LoRa-based networks, packet loss is a common problem resulting from limited bandwidth and transmission interference. During image transmission, it leads to missing data in the received content, most often visible as vertical or horizontal lines. We present a method to repair such corrupted images using a fully convolutional neural network inspired by the U-Net architecture. The experiments carried out show that the proposed approach effectively reconstructs missing parts of the image, achieving high structural similarity (SSIM) to the original. The proposed method can be applied to image transmission and reconstruction on low-power devices, typical of IoT systems that use LoRa for communication.
Recommended Citation
Nowak, J., Nowak, T., Połcik, S., Korytkowski, M., Drozda, P. & Scherer, R. (2025). Reconstruction of Images Transmitted over LoRa Networks Using Fully Convolutional Neural NetworkIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.76