@article{article_1639203, title={Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques}, journal={Sakarya University Journal of Computer and Information Sciences}, volume={8}, pages={358–381}, year={2025}, url={https://izlik.org/JA32HZ47LS}, author={Altunkaya, Duygu and Yıldırım Okay, Feyza and Özdemir, Suat}, keywords={Internet of Things, Time-series, Image transformation, Image encoding}, abstract={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.}, number={2}