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arXiv · 2311.12742

Image Transformation for IoT Time-Series Data: A Review

Duygu Altunkaya·Feyza Yildirim Okay·Suat Ozdemir
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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.

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Duygu Altunkaya, Feyza Yildirim Okay, Suat Ozdemir. 2023-11-21. Image Transformation for IoT Time-Series Data: A Review. https://arxiv.org/abs/2311.12742

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