Encoding IoT Data: a Comprehensive Review of Image Transformation Techniques

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 that use image transformation/encoding techniques in the 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. © 2025, Sakarya University. All rights reserved.