Abstract
With the advancement of science and technology in recent years, the Internet of Things has become another technology hotspot after the Internet. It is widely used in various fields under its intelligent processing and reliability of transmission. However, rapid development also brings certain opportunities and challenges. The most prominent is the massive increase in equipment data, which brings huge challenges to the field of data analysis and prediction. Therefore, how to efficiently process and predict the time series data generated by the Internet of Things has become a research hotspot and difficulty. With the improvement of computer indicators in the past ten years, machine learning has developed to a certain extent. Most scholars will use machine learning methods when researching time-series data processing and forecasting of the Internet of Things. Therefore, we provide a preliminary overview of the history and evolution of machine learning-based IoT time-series data analysis and forecasting from a bibliometric perspective.
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References
Sun, B., Geng, R., Zhang, L., Li, S., Shen, T., Ma, L.: Securing 6G-enabled IoT/IoV networks by machine learning and data fusion. EURASIP J. Wirel. Commun. Network. 2022 (2022)
Sun, B., Geng, R., Shen, T., Xu, Y., Bi, S.: Dynamic emergency transit forecasting with IoT sequential data. Mob. Netw. Appl. 1–15 (2022)
Chen, M., Li, W., Hao, Y., Qian, Y., Humar, I.: Edge cognitive computing based smart healthcare system. Futur. Gener. Comput. Syst. 86, 403–411 (2018)
Sun, B., Ma, L., Shen, T., Geng, R., Zhou, Y., Tian, Y.: A robust data-driven method for multiseasonality and heteroscedasticity in time series preprocessing. Wirel. Commun. Mob. Comput. 2021, 6692390:1–6692390:11 (2021)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Wu, B., Wan, A., Iandola, F., Jin, P.H., Keutzer, K.: Squeezedet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 446–454 (2017)
Sun, B., Geng, R., Yuan, X., Shen, T.: Prediction of emergency mobility under diverse IoT availability. EAI Endors. Trans. Pervasive Health Technol. 8(4), e2 (2022)
Yan, S., Shao, H., Xiao, Y., Liu, B., Wan, J.: Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises. Robot. Comput. Integr. Manuf. 79, 102441 (2023)
Chen, M., Shao, H., Dou, H., Li, W., Liu, B.: Data augmentation and intelligent fault diagnosis of planetary gearbox using Ilofgan under extremely limited samples. IEEE Trans. Reliab. 1–9 (2022)
Aria, M., Cuccurullo, C.: bibliometrix: An r-tool for comprehensive science mapping analysis. J. Informet. 11(4), 959–975 (2017)
Nisonger, T.E.: The “80/20 rule” and core journals. Serials Librarian 55(1-2), 62–84 (2008)
Tang, F., Fadlullah, Z.Md., Mao, B., Kato, N.: An intelligent traffic load prediction-based adaptive channel assignment algorithm in SDN-IoT: a deep learning approach. IEEE Internet Things J. 5(6), 5141–5154 (2018)
Wang, X., Wang, C., Li, X., Leung, V.C.M., Taleb, T.: Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching. IEEE Internet Things J. 7(10), 9441–9455 (2020)
Luong, N.C., et al.: Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun. Surv. Tutor. 21(4), 3133–3174 (2019)
Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., Sheth, A.P.: Machine learning for internet of things data analysis: a survey. Digit. Commun. Netw. 4(3), 161–175 (2018)
Tang, F., Kawamot, Y., Kato, N., Liu, J.: Future intelligent and secure vehicular network toward 6g: machine-learning approaches. Proc. IEEE 108(2), 292–307 (2020)
Munir, M., Siddiqui, S.A., Dengel, A., Ahmed, S.: DeepAnt: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991–2005 (2019)
Lotka, A.J.: The frequency distribution of scientific productivity. J. Washington Acad. Sci. (Baltimore) 19, 317–323 (1926)
Sun, B., Ma, L., Geng, R., Xu, Y.: Matrix profile evolution: an initial overview. In: Fu, W., Xu, Y., Wang, S.-H., Zhang, Y. (eds.) ICMTEL 2021. LNICST, vol. 387, pp. 492–501. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-82562-1_48
Sun, B., Cheng, W., Bai, G., Goswami, P.: Correcting and complementing freeway traffic accident data using Mahalanobis distance based outlier detection. Tehnicki Vjesnik-Tech. Gazette 24, 10 (2017)
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Zhang, L., Li, L., Dong, B., Ma, Y., Liu, Y. (2024). Understanding the Trend of Internet of Things Data Prediction. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_27
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