saucis Sakarya University Journal of Computer and Information Sciences 2636-8129 Sakarya University Software Engineering (Other) Yazılım Mühendisliği (Diğer) Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques https://orcid.org/0000-0002-4277-056X Altunkaya Duygu MARDIN ARTUKLU UNIVERSITY https://orcid.org/0000-0002-6239-3722 Yıldırım Okay Feyza GAZI UNIVERSITY https://orcid.org/0000-0002-4588-4538 Özdemir Suat HACETTEPE UNIVERSITY 06 30 2025 8 2 358 381 02 19 2025 04 08 2025 Copyright © 2018, Sakarya University Journal of Computer and Information Sciences 2018 Sakarya University Journal of Computer and Information Sciences

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.

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