Szczegóły publikacji
Opis bibliograficzny
Reconstruction of images transmitted over LoRa networks using fully convolutional neural network / Jakub Nowak, Tomasz Nowak, Szymon Połcik, Marcin Korytkowski, Paweł Drozda, Rafał SCHERER // W: ISD2025 [Dokument elektroniczny] : [33rd international conference on Information Systems Development] : September 3-5, 2025, Belgrade, Serbia] : empowering the interdisciplinary role of ISD in addressing contemporary issues in digital transformation: how data science and generative AI contributes to ISD? : proceedings / eds. I. Luković, [et al.]. — Wersja do Windows. — Dane tekstowe. — Gdańsk : University of Gdańsk ; Belgrade : University of Belgrade, 2025. — ( Proceedings of the International Conference on Information Systems Development ; ISSN 2938-5202 ). — e-ISBN: 978-83-972632-1-5. — S. [1–8]. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1755&con... [2025-12-04]. — Bibliogr. s. [8], Abstr. — R. Scherer - dod. afiliacja: Czestochowa University of Technology, Faculty of Computer Science and Artificial Intelligence, Czestochowa, Poland ; Center of Excellence in Artificial Intelligence
Autorzy (6)
- Nowak Jakub
- Nowak Tomasz
- Połcik Szymon
- Korytkowski Marcin
- Drozda Paweł
- AGHScherer Rafał
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164726 |
|---|---|
| Data dodania do BaDAP | 2025-12-15 |
| DOI | 10.62036/ISD.2025.76 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Uniwersytet Gdański |
| Konferencja | International Conference on Information Systems Development 2025 |
| Czasopismo/seria | Proceedings of the International Conference on Information Systems Development |
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.
