In the era of the Internet of Things (IoT), where smartphones, built-in
systems, wireless sensors, and nearly every smart device connect through local
networks or the internet, billions of smart things communicate with each other
and generate vast amounts of time-series data. As IoT time-series data is
high-dimensional and high-frequency, time-series classification or regression
has been a challenging issue in IoT. Recently, deep learning algorithms have
demonstrated superior performance results in time-series data classification in
many smart and intelligent IoT applications. However, it is hard to explore the
hidden dynamic patterns and trends in time-series. Recent studies show that
transforming IoT data into images improves the performance of the learning
model. In this paper, we present a review of these studies which use image
transformation/encoding techniques in IoT domain. We examine the studies
according to their encoding techniques, data types, and application areas.
Lastly, we emphasize the challenges and future dimensions of image
transformation.

在物联网时代，物联网数据的高维和高频率给时序分类或回归带来了挑战，而深度学习算法在智能物联网应用中表现出了优异的时间序列数据分类性能。然而，发现时序中隐藏的动态模式和趋势仍然困难。近期的研究表明，将物联网数据转换为图像可以提高学习模型的性能。本文介绍了在物联网领域中使用图像转换 / 编码技术的研究进展，并根据其编码技术、数据类型和应用领域进行了综述。最后，我们强调了图像转换面临的挑战和未来的发展方向。