Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques

• Published in: Sakarya university journal of computer and information sciences Vol. 8; no. 2; pp. 358 - 381
• Main Authors: Suat Özdemir, Feyza Yıldırım Okay, Duygu Altunkaya
• Format: Journal Article
• Language: English
• Published: Sakarya University 30.06.2025
• Subjects: image encoding; image transformation; internet of things; time-series
• ISSN: 2636-8129
• DOI: 10.35377/saucis..1639203

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.