Review
BibTex RIS Cite

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

References

  • S. Cc, “A survey on architecture, protocols and challenges in IoT,” Wireless Personal Communications, vol. 112, 06 2020.
  • D. I. Borissova, V. K. Danev, M. B. Rashevski, I. G. Garvanov, R. D. Yoshinov, and M. Z. Garvanova, “Using IoT for automated heating of a smart home by means of openhab software platform,” IFAC-PapersOnLine, vol. 55, no. 11, pp. 90–95, 2022, iFAC Workshop on Control for Smart Cities CSC 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405896322011430
  • A. Aditya, S. Anwarul, R. Tanwar, and S. K. V. Koneru, “An IoT-assisted intelligent parking system (IPS) for smart cities,” Procedia Computer Science, vol. 218, pp. 1045–1054, 2023, International Conference on Machine Learning and Data Engineering. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050923000844
  • S. R., R. M, V. S, S. K. E, Y. S, A. Kumar, J. R. I., and V. K, “A novel autonomous irrigation system for smart agriculture using AI and 6 G-enabled IoT network,” Microprocessors and Microsystems, vol. 101, p. 104905, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0141933123001497
  • S. Juyal, S. Sharma, and A. Shankar Shukla, “Smart skin health monitoring using AI-enabled cloud-based IoT,” Materials Today: Proceedings, vol. 46, pp. 10 539–10 545, 2021, International Conference on Technological Advancements in Materials Science and Manufacturing. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2214785321000973
  • A. Hassan, M. S. Abdul Rahman, W. Md Shah, M. F. I. Othman, and F. Mansourkiaie, “Internet of things based smart shelves prototype implementation,” Journal of Advanced Computing Technology and Application (JACTA), vol. 2, no. 1, pp. 9–14, May 2020. [Online]. Available: https://jacta.utem.edu.my/jacta/article/view/5208
  • A. Malki, E.-S. Atlam, and I. Gad, “Machine learning approach of detecting anomalies and forecasting time-series of IoT devices,” Alexandria Engineering Journal, vol. 61, no. 11, pp. 8973–8986, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1110016822001260
  • M. Herrera, M. Sasidharan, J. Merino, and A. K. Parlikad, “Handling irregularly sampled IoT time series to inform infrastructure asset management,” IFAC-PapersOnLine, vol. 55, no. 19, pp. 241–245, 2022, 5th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies AMEST 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S240589632201432X
  • Y. Liu, D. Sun, R. Zhang, and W. Li, “A Method for Detecting Ldos Attacks Based on Hilbert-Huang Transform and Convolutional Neural Network,” SSRN Electronic Journal, 2023.
  • M. Shahin, F. Chen, H. Bouzary, A. Hosseinzadeh, and R. Rashidifar, “A novel fully convolutional neural network approach for detection and classification of attacks on industrial IoT devices in smart manufacturing systems,” The International Journal of Advanced Manufacturing Technology, vol. 123, 10 2022.
  • J. Lin, J. Li, and J. Chen, “An analysis of English classroom behavior by intelligent image recognition in IoT,” International Journal of System Assurance Engineering and Management, vol. 13, no. 3, pp. 1063–1071, December 2022.
  • J. Hong and J. Yoon, “Multivariate time-series classification of sleep patterns using a hybrid deep learning architecture,” in 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), 2017, pp. 1–6.
  • Y. Xu, Y. Tang, and Q. Yang, “Deep learning for IoT intrusion detection based on LSTMs-ae,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacturing, ser. AIAM2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 64–68. [Online]. Available: https://doi.org/10.1145/3421766.3421891
  • J. Yang, Y. Sun, Y. Chen, M. Mao, L. Bai, and S. Zhang, “Time series-to-image encoding for saturation line prediction using channel and spatial-wise attention network,” Expert Systems with Applications, vol. 237, p. 121440, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417423019425
  • J. Li and Q. Wang, “Comparison of the representational ability in individual difference analysis using 2-d time-series image and time-series feature patterns,” Expert Systems with Applications, vol. 215, p. 119429, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417422024484
  • C. Velasco-Gallego and I. Lazakis, “Development of a time series imaging approach for fault classification of marine systems,” Ocean Engineering, vol. 263, p. 112297, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0029801822016006
  • G. Chiarot and C. Silvestri, “Time Series Compression Survey,” ACM Computing Surveys, vol. 55, no. 10 Feb 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3560814
  • X. Wan, H. Li, L. Zhang, and Y. J. Wu, “Dimensionality reduction for multivariate time-series data mining,” Journal of Supercomputing, vol. 78, no. 7, pp. 9862–9878, 2022. [Online]. Available: https://doi.org/10.1007/s11227-021-04303-4
  • X. Yu, X. Yang, Q. Tan, C. Shan, and Z. Lv, “An edge computing-based anomaly detection method in IoT industrial sustainability,” Applied Soft Computing, vol. 128, p. 109486, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1568494622005841
  • M. Neyja, S. Mumtaz, K. M. S. Huq, S. A. Busari, J. Rodriguez, and Z. Zhou, “An iot-based e-health monitoring system using ecg signal,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017, pp. 1–6.
  • J. Azar, A. Makhoul, R. Couturier, and J. Demerjian, “Robust iot time series classification with data compression and deep learning,” Neurocomputing, vol. 398, pp. 222–234, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231220302939
  • G. Chen, M. Liu, and Z. Kong, “Temporal-logic-based semantic fault diagnosis with time-series data from industrial internet of things,” IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 4393–4403, 2021.
  • I. Niyonambaza, M. Zennaro, and A. Uwitonze, “Predictive maintenance (pdm) structure using internet of things (iot) for mechanical equipment used into hospitals in rwanda,” Future Internet, vol. 12, no. 12, 2020. [Online]. Available: https://www.mdpi.com/1999-5903/12/12/224
  • J. Stankovic, “Research directions for the internet of things,” Internet of Things Journal, IEEE, vol. 1, pp. 3–9, 02 2014.
  • A. Cook, G. Misirli, and Z. Fan, “Anomaly detection for iot time-series data: A survey,” IEEE Internet of Things Journal, vol. PP, pp. 1–1, 12 2019.
  • L. Chettri and R. Bera, “A comprehensive survey on internet of things (iot) toward 5g wireless systems,” IEEE Internet of Things Journal, vol. 7, no. 1, pp. 16–32, 2020.
  • N. Yen, J.-W. Chang, J.-Y. Liao, and Y.-M. Yong, “Analysis of interpolation algorithms for the missing values in iot time series: a case of air quality in taiwan,” The Journal of Supercomputing, vol. 76, 08 2020.
  • Y. Liu, Y. Zhou, K. Yang, and X. Wang, “Unsupervised Deep Learning for IoT Time Series,” IEEE Internet of Things Journal, 2023.
  • H. Kaur, D. Koundal, and V. Kadyan, “Image fusion techniques: A survey,” Archives of Computational Methods in Engineering, vol. 28, pp. 4425 – 4447, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:231700128
  • D. Sudha and M. Ramakrishna, “Comparative study of features fusion techniques,” 03 2017, pp. 235–239.
  • J. Chen and X. Wang, “Non-intrusive Load Monitoring Using Gramian Angular Field Color Encoding in Edge Computing,” Chinese Journal of Electronics, vol. 31, no. 4, pp. 595–603, 2022.
  • H. Zhou and C. Kan, “Tensor-based ecg anomaly detection toward cardiac monitoring in the internet of health things,” Sensors, vol. 21, no. 12, 2021.
  • P. K. Atrey, M. A. Hossain, A. E. Saddik, and M. Kankanhalli, “Multimodal fusion for multimedia analysis: a survey,” Multimedia Systems, vol. 16, pp. 345–379, 2010. [Online]. Available: https://api.semanticscholar.org/CorpusID:6387482
  • W. Jiang, D. Zhang, L. Ling, and R. Lin, “Time Series Classification Based on Image Transformation Using Feature Fusion Strategy,” Neural Processing Letters, vol. 54, no. 5, pp. 3727–3748, oct 2022. [Online]. Available: https://link.springer.com/article/10.1007/s11063-022-10783-z
  • M. Ehatisham-Ul-Haq, A. Javed, M. A. Azam, H. M. A. Malik, A. Irtaza, I. H. Lee, and M. T. Mahmood, “Robust human activity recognition using multimodal feature-level fusion,” IEEE Access, vol. 7, pp. 60 736–60 751, 2019.
  • Y. Han, B. Li, Y. Huang, and L. Li, “Bearing fault diagnosis method based on gramian angular field and ensemble deep learning,” Journal of Vibroengineering, vol. 25, no. 1, pp. 42–52, oct 2022. [Online]. Available: https://doi.org/10.21595/jve.2022.22796
  • H. Wei and N. Kehtarnavaz, “Simultaneous utilization of inertial and video sensing for action detection and recognition in continuous action streams,” IEEE Sensors Journal, vol. 20, no. 11, pp. 6055–6063, 2020.
  • R. Hang, Z. Li, P. Ghamisi, D. Hong, G. Xia, and Q. Liu, “Classification of hyperspectral and lidar data using coupled cnns,” CoRR, vol. abs/2002.01144, 2020. [Online]. Available: https://arxiv.org/abs/2002.01144
  • G. Baldini, R. Giuliani, and F. Dimc, “Physical layer authentication of Internet of Things wireless devices using convolutional neural networks and recurrence plots,” Internet Technology Letters, vol. 2, no. 2, p. e81, mar 2019. [Online]. Available: https://onlinelibrary.wiley.com/doi/full/10.1002/itl2.81https: //onlinelibrary.wiley.com/doi/abs/10.1002/itl2.81https://onlinelibrary.wiley.com/doi/10.1002/itl2.81
  • C. L. Yang, Z. X. Chen, and C. Y. Yang, “Sensor Classification Using Convolutional Neural Network by Encoding Multivariate Time Series as Two-Dimensional Colored Images,” Sensors 2020, Vol. 20, Page 168, vol. 20, no. 1, p. 168, dec 2019. [Online]. Available: https://www.mdpi.com/1424-8220/20/1/168/htmhttps: //www.mdpi.com/1424-8220/20/1/168
  • R. John, S. Vijayaraghavan, and R. Pradeep, “A Modern Cardiac Monitoring System Based on Wireless Sensing for Human Health Care,” Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019, pp. 750–756, 2019.
  • M. Fahim, K. Fraz, and A. Sillitti, “Tsi: Time series to imaging based model for detecting anomalous energy consumption in smart buildings,” Information Sciences, vol. 523, pp. 1–13, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0020025520301596
  • C. Lyu, Z. Huo, X. Cheng, J. Jiang, A. Alimasi, and H. Liu, “Distributed Optical Fiber Sensing Intrusion Pattern Recognition Based on GAF and CNN,” JOURNAL OF LIGHTWAVE TECHNOLOGY, vol. 38, no. 15, 2020. [Online]. Available: https://www.ieee.org/publications/rights/index.html
  • A. Estebsari and R. Rajabi, “Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques,” Electronics 2020, Vol. 9, Page 68, vol. 9, no. 1, p. 68, jan 2020. [Online]. Available: https://www.mdpi.com/2079-9292/9/1/68/htmhttps://www.mdpi.com/2079-9292/9/1/68
  • A. Ferraro, A. Galli, V. Moscato, and G. Sperli, “A novel approach for predictive maintenance combining GAF encoding strategies and deep networks,” Proceedings - 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, DependSys 2020, pp. 127–132, 2020.
  • H. Xu, J. Li, H. Yuan, Q. Liu, S. Fan, T. Li, and X. Sun, “Human activity recognition based on gramian angular field and deep convolutional neural network,” IEEE Access, vol. 8, pp. 199 393–199 405, 2020.
  • K. Vandith Sreenivas, M. Ganesan, and R. Lavanya, “Classification of Arrhythmia in Time Series ECG Signals Using Image Encoding and Convolutional Neural Networks,” Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021, 2021.
  • B. Zhu, X. Hou, S. Liu, W. Ma, M. Dong, H. Wen, Q. Wei, S. Du, and Y. Zhang, “IoT Equipment Monitoring System Based on C5.0 Decision Tree and Time-series Analysis,” IEEE Access, vol. 10, pp. 1–1, jan 2021. [Online]. Available: https://typeset.io/papers/iot-equipment-monitoring-system-based-on-c5-0-decision-tree-54megjwwoi
  • A. K. A, G. M, and L. R, “Emotional classification of eeg signal using image encoding and deep learning,” in 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), 2021, pp. 1–5.
  • N. Sharma, M. Mangla, S. N. Mohanty, D. Gupta, P. Tiwari, M. Shorfuzzaman, and M. Rawashdeh, “A smart ontology-based IoT framework for remote patient monitoring,” Biomedical Signal Processing and Control, vol. 68, p. 102717, 2021. [Online]. Available: https://doi.org/10.1016/j.bspc.2021.102717
  • Y. Chen, H. Deng, D. Zhang, and Y. Hu, “SpeedNet: Indoor Speed Estimation with Radio Signals,” IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2762–2774, feb 2021.
  • J. R. Jiang and C. T. Yen, “Product quality prediction for wire electrical discharge machining with markov transition fields and convolutional long short-term memory neural networks,” Applied Sciences (Switzerland), vol. 11, no. 13, 2021.
  • G. R. Garcia, G. Michau, M. Ducoffe, J. S. Gupta, and O. Fink, “Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms,” arXiv: Learning, vol. 236, no. 4, pp. 617–627, may 2020. [Online]. Available: https://typeset.io/papers/ time-series-to-images-monitoring-the-condition-of-industrial-2c2m2v1l2a
  • T. Huang, P. Chakraborty, and A. Sharma, “Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images,” International Journal of Transportation Science and Technology, vol. 12, no. 1, pp. 1–18, 2023. [Online]. Available: https://doi.org/10.1016/j.ijtst.2021.10.007
  • Q. Jiang, S. Zhang, J. Chen, X. Chen, H. Huang, and C. Gu, “Adaptive dila-densenet for image based time series classification in iot,” in 2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1–8.
  • R. Singh, T. Ahmed, A. Kumar Singh, P. Chanak, and S. K. Singh, “SeizSClas: An Efficient and Secure Internet of-Things-Based EEG Classifier,” IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6214–6221, apr 2021.
  • A. De Santo, A. Ferraro, A. Galli, V. Moscato, and G. Sperl`ı, “Evaluating time series encoding techniques for Predictive Maintenance,” Expert Systems with Applications, vol. 210, no. August, p. 118435, 2022. [Online]. Available: https://doi.org/10.1016/j.eswa.2022.118435
  • B. Bertalanič, M. Meza, and C. Fortuna, “Resource-Aware Time Series Imaging Classification for Wireless Link Layer Anomalies,” IEEE Transactions on Neural Networks and Learning Systems, vol. 14, no. 8, pp. 1–13, 2022.
  • A. Alsalemi, A. Amira, H. Malekmohamadi, and K. Diao, “Lightweight Gramian Angular Field classification for edge internet of energy applications,” Cluster Computing, vol. 26, no. 2, pp. 1375–1387, 2022. [Online]. Available: https://doi.org/10.1007/s10586-022-03704-1
  • P. Zhang, Y. Hang, X. Ye, P. Guan, J. Jiang, J. Tan, and W. Hu, “A United CNN-LSTM Algorithm Combining RR Wave Signals to Detect Arrhythmia in the 5G-Enabled Medical Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 16, pp. 14 563–14 571, 2022.
  • S. Dou, S. Shao, C. Song, H. Shi, and H. Zhao, “Electrocardiogram Signal Classification Algorithm Based on The Continuous Wavelet Transform and GoogleNet in an Internet of Things Context,” Journal of Mechanics in Medicine and Biology, vol. 22, no. 9, p. 2240049, nov 2022. [Online]. Available: www.worldscientific.com
  • M. Abdel-Basset, H. Hawash, V. Chang, R. K. Chakrabortty, and M. Ryan, “Deep Learning for Heterogeneous Human Activity Recognition in Complex IoT Applications,” IEEE Internet of Things Journal, vol. 9, no. 8, pp. 5653–5665, 2022.
  • B. Wang, G. Feng, D. Huo, and Y. Kang, “A Bearing Fault Diagnosis Method Based on Spectrum Map Information Fusion and Convolutional Neural Network,” Processes, vol. 10, no. 7, 2022.
  • T. Bai, X. Li, and S. Ding, “Research on Electrical Parameter Fault Diagnosis Method of Oil Well Based on TSC DCGAN Deep Learning,” 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022, pp. 753–761, 2022.
  • A. Abidi, D. Ienco, A. B. Abbes, and I. R. Farah, “Combining 2D encoding and convolutional neural network to enhance land cover mapping from Satellite Image Time Series,” Engineering Applications of Artificial Intelligence, vol. 122, no. March, p. 106152, 2023. [Online]. Available: https://doi.org/10.1016/j.engappai.2023.106152
  • P. O. de Paula, T. B. da Silva Costa, R. R. de Faissol Attux, and D. G. Fantinato, “Classification of image encoded SSVEP-based EEG signals using Convolutional Neural Networks,” Expert Systems with Applications, vol. 214, no. July 2022, p. 119096, 2023. [Online]. Available: https://doi.org/10.1016/j.eswa.2022.119096
  • S. Quan, M. Sun, X. Zeng, X. Wang, and Z. Zhu, “Time Series Classification Based on Multi-Dimensional Feature Fusion,” IEEE Access, vol. 11, pp. 11 066–11 077, 2023.
  • Y. Zhang, L. Shang, H. Gao, Y. He, X. Xu, and Y. Chen, “A New Method for Diagnosing Motor Bearing Faults Based on Gramian Angular Field Image Coding and Improved CNN-ELM,” IEEE Access, vol. 11, pp. 11 337–11 349, 2023.
  • A. Copiaco, Y. Himeur, A. Amira, W. Mansoor, F. Fadli, S. Atalla, and S. S. Sohail, “An innovative deep anomaly detection of building energy consumption using energy time-series images,” Engineering Applications of Artificial Intelligence, vol. 119, no. September 2022, p. 105775, 2023. [Online]. Available: https://doi.org/10.1016/j.engappai.2022.105775
  • L. Qu, Y. Kong, M. Li, W. Dong, F. Zhang, and H. Zou, “A residual convolutional neural network with multi-block for appliance recognition in non-intrusive load identification,” Energy and Buildings, vol. 281, p. 112749, feb 2023.
  • X. Sun, M. Wang, B. Zhan, Y. Xiong, and W. Yu, “An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems,” Shock and Vibration, vol. 2023, 2023.
  • A. N. Sayed, Y. Himeur, and F. Bensaali, “From time-series to 2D images for building occupancy prediction using deep transfer learning,” Engineering Applications of Artificial Intelligence, vol. 119, no. January, p. 105786, 2023. [Online]. Available: https://doi.org/10.1016/j.engappai.2022.105786
  • M. N. Hasan, S. U. Jan, and I. Koo, “Wasserstein GAN-based Digital Twin Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks,” IEEE Sensors Journal, vol. XX, no. Xx, pp. 1–14, 2023.
  • M. Hammoud, E. Kovalenko, A. Somov, E. Bril, and A. Baldycheva, “Deep learning framework for neurological diseases diagnosis through near-infrared eye video and time series imaging algorithms,” Internet of Things, vol. 24, p. 100914, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2542660523002378
  • Y. Yan, Y. Yang, F. Shen, M. Gao, and Y. Gu, “Gde model: A variable intrusion detection model for few-shot attack,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 10, p. 101796, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1319157823003506
  • Z. Wang and T. Oates, “Encoding time series as images for visual inspection and classification using tiled convolu tional neural networks,” AAAI Workshop - Technical Report, vol. WS-15-14, no. January, pp. 40–46, 2015.
  • J. P. Eckmann, O. Oliffson Kamphorst, and D. Ruelle, “Recurrence plots of dynamical systems,” Epl, vol. 4, no. 9, pp. 973–977, 1987.
  • K. Gr¨ochenig, The Short-Time Fourier Transform. Boston, MA: Birkh¨auser Boston, 2001, pp. 37–58.
  • V. J. Bol´os and R. Ben´ıtez, The Wavelet Scalogram in the Study of Time Series. Cham: Springer International Publishing, 2014, pp. 147–154.
  • N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London Series A, vol. 454, no. 1971, pp. 903–998, Mar. 1998.
  • L. Wen, X. Li, L. Gao, and Y. Zhang, “A new convolutional neural network-based data-driven fault diagnosis method,” IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5990–5998, 2018.
  • L. De Baets, J. Ruyssinck, C. Develder, T. Dhaene, and D. Deschrijver, “Appliance classification using vi trajectories and convolutional neural networks,” Energy and Buildings, vol. 158, pp. 32–36, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0378778817312690
  • Z. Yang, I. A. Abbasi, F. Algarni, S. Ali, and M. Zhang, “An IoT Time Series Data Security Model for Adversarial Attack Based on Thermometer Encoding,” Security and Communication Networks, vol. 2021, 2021.
  • C.-L. Fan and J.-R. Jiang, “Surface roughness prediction based on markov chain and deep neural network for wire electrical discharge machining,” in 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), 2019, pp. 191–194.
  • C. Fu, M. Quintana, Z. Nagy, and C. Miller, “Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation,” Applied Thermal Engineering, vol. 236, p. 121545, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1359431123015740
  • Z. Guo, Y. Wan, and H. Ye, “A data imputation method for multivariate time series based on generative adversarial network,” Neurocomputing, vol. 360, pp. 185–197, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231219308306
  • M. Ghahramani, R. Taheri, M. Shojafar, S. Member, R. Javidan, and S. Wan, “Deep Image: A precious image-based deep learning method for online malware detection in IoT Environment,” 2022.
  • A. Ghasemieh and R. Kashef, “A robust deep learning model for predicting the trend of stock market prices during market crash periods,” in 2022 IEEE International Systems Conference (SysCon), 2022, pp. 1–8.
  • L. Yu, J. Li, T. Wang, F. Tan, C. He, and H. Song, “T2i-net: Time series classification via deep sequence-to-image transformation networks,” in 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC), 2022, pp. 1–5.
  • J. Debayle, N. Hatami, and Y. Gavet, “Classification of time-series images using deep convolutional neural net works,” 04 2018, p. 23.
  • X. Yuan, D. Tanksley, P. Jiao, L. Li, G. Chen, and D. Wunsch, “Encoding Time-Series Ground Motions as Images for Convolutional Neural Networks-Based Seismic Damage Evaluation,” Frontiers in Built Environment, vol. 7, p. 52, apr 2021.
  • K. S. Kiangala and Z. Wang, “An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment,” IEEE Access, vol. 8, pp. 121 033–121 049, 2020.
  • M. Ashraf, F. Anowar, J. H. Setu, A. I. Chowdhury, E. Ahmed, A. Islam, and A. Al-Mamun, “A survey on dimensionality reduction techniques for time-series data,” IEEE Access, vol. 11, pp. 42 909–42 923, 2023.
  • H. Jiang, L. Liu, and C. Lian, “Multi-modal fusion transformer for multivariate time series classification,” in 2022 14th International Conference on Advanced Computational Intelligence (ICACI), 2022, pp. 284–288.
  • H. Nizam, S. Zafar, Z. Lv, F. Wang, and X. Hu, “Real-time deep anomaly detection framework for multivariate time-series data in industrial iot,” IEEE Sensors Journal, vol. 22, no. 23, pp. 22 836–22 849, 2022.
  • I. Ali, H. Bayomi, and K. Wassif, “Dimensionality reduction for images of iot using machine learning,” 2023. [Online]. Available: https://doi.org/10.21203/rs.3.rs-2666777/v1
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 2025 Volume: 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 (June2025), 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.
 Download Cover Image


INDEXING & ABSTRACTING & ARCHIVING


 31045 31044   ResimLink - Resim Yükle  31047 

31043 28939 28938 34240


29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License