Image Transformation for IoT Time-Series Data: A Review
Duygu Altunkaya Feyza Yildirim Okay Suat Ozdemir
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.