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Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques

Year 2025, Volume: 8 Issue: 2, 358 - 381, 30.06.2025

Duygu Altunkaya , Feyza Yıldırım Okay , Suat Özdemir

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.

Keywords

Internet of Things Time-series Image transformation Image encoding

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There are 96 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Review
Authors

Duygu Altunkaya 0000-0002-4277-056X

Feyza Yıldırım Okay 0000-0002-6239-3722

Suat Özdemir 0000-0002-4588-4538

Early Pub Date June 30, 2025
Publication Date June 30, 2025
Submission Date February 19, 2025
Acceptance Date April 8, 2025
Published in Issue Year 2025Volume: 8 Issue: 2

Cite

APA Altunkaya, D., Yıldırım Okay, F., & Özdemir, S. (2025). Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques. Sakarya University Journal of Computer and Information Sciences, 8(2), 358-381.
AMA Altunkaya D, Yıldırım Okay F, Özdemir S. Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques. SAUCIS. June 2025;8(2):358-381.
Chicago Altunkaya, Duygu, Feyza Yıldırım Okay, and Suat Özdemir. “Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques”. Sakarya University Journal of Computer and Information Sciences 8, no. 2 (June 2025): 358-81.
EndNote Altunkaya D, Yıldırım Okay F, Özdemir S (June 1, 2025) Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques. Sakarya University Journal of Computer and Information Sciences 8 2 358–381.
IEEE D. Altunkaya, F. Yıldırım Okay, and S. Özdemir, “Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques”, SAUCIS, vol. 8, no. 2, pp. 358–381, 2025.
ISNAD Altunkaya, Duygu et al. “Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 2025), 358-381.
JAMA Altunkaya D, Yıldırım Okay F, Özdemir S. Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques. SAUCIS. 2025;8:358–381.
MLA Altunkaya, Duygu et al. “Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, 2025, pp. 358-81.
Vancouver Altunkaya D, Yıldırım Okay F, Özdemir S. Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques. SAUCIS. 2025;8(2):358-81.
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