Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques

Duygu Altunkaya; Feyza Yıldırım Okay; Suat Özdemir

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 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

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Review

Authors

Duygu Altunkaya ^*
0000-0002-4277-056X
Türkiye

Feyza Yıldırım Okay
0000-0002-6239-3722
Türkiye

Suat Özdemir
0000-0002-4588-4538
Türkiye

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 2025 Volume: 8 Number: 2

IZ

https://izlik.org/JA32HZ47LS

Cite

RIS / Bibtex

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. https://izlik.org/JA32HZ47LS

AMA

1.Altunkaya D, Yıldırım Okay F, Özdemir S. Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques. SAUCIS. 2025;8(2):358-381. https://izlik.org/JA32HZ47LS

Chicago

Altunkaya, Duygu, Feyza Yıldırım Okay, and Suat Özdemir. 2025. “Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques”. Sakarya University Journal of Computer and Information Sciences 8 (2): 358-81. https://izlik.org/JA32HZ47LS.

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

[1]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, June 2025, [Online]. Available: https://izlik.org/JA32HZ47LS

ISNAD

Altunkaya, Duygu - Yıldırım Okay, Feyza - Özdemir, Suat. “Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 358-381. https://izlik.org/JA32HZ47LS.

JAMA

1.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, June 2025, pp. 358-81, https://izlik.org/JA32HZ47LS.

Vancouver

1.Duygu Altunkaya, Feyza Yıldırım Okay, Suat Özdemir. Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques. SAUCIS [Internet]. 2025 Jun. 1;8(2):358-81. Available from: https://izlik.org/JA32HZ47LS