Evaluación de Algoritmos de Compresión de Imágenes para Transmisión vía LoRa

Abstract

Abstract: This work evaluates different image compression algorithms to optimize image transmission via LoRa technology, enabling improved Artificial Intelligence of Things (AIoT) applications in remote areas. Traditional and neural network-based methods are investigated, taking into account metrics such as signal-to-noise ratio, bits per pixel, multiscale structural similarity index, transmission time, and energy efficiency. The curriculum integration work is also tested in controlled and real-world environments, with the mbt2018-mean compression model showing a fairly high efficiency in terms of balancing fidelity, compression time, and energy consumption. This study is part of the Chapak project, contributing to the development of efficient IoT transmission in telecommunications.