Real-Time High-Speed High Dimension Data Streaming and Feature Extraction on Edge Computing Devices in Industrial Internet of Things (IIoT)
Keywords:
Industrial Internet of Things, Information Flow of IIoT, Streaming Process, Enhanced BIRCH ClusteringAbstract
Industrial Internet of Things (IIoT) and Artificial Internet of Things (AIoT) attracted technologist due to its economical impact and advancement in prediction and decision making using collected data. Data collection and data processing in the IIoT and AIoT is experiencing its limitations in computational devices and storage devices. IIoT and AIoT pumps large amount of data to the network in the form of high dimension and high speed, the pumped data is collected processed and feature extracted. Key Challenge of the collected data is to extract insights (Features) about it, huge amount of data collection and data processing has become a most interesting and open problem statement for both Industrialist and Researchers. IIoT system is becoming a primary area for data mining research, most sophisticated data analysis tools are required to understand the stress of data coming for the IIoT sensors and the devices connected to the network. More the devices connected to the network, more the data we need to process at a given time. This makes the data mining more complex in the IIoT environment. As the data is collected for longer duration, there is huge chance of data drift; this is one of the core issues in the IIoT data streaming and mining system. This paper proposes an efficient data mining and feature extraction technique for IIoT, The proposed technique reduce the computation significantly and increase the feature extractions on Edge computing. The author proposes a frame work for IIoT data processing named as Information Flow of IIoT (IFIIoT). The Proposed Framework is assessed using both synthetic and real-world data, encompassing various streaming speeds and data drift scenarios. The evaluation takes into account the overall performance of existing state-of-the-art algorithms in the literature, while the generated data provides valuable insights into the clustering system. Consequently, the proposed framework demonstrates superior performance compared to the current state-of-the-art algorithms described in the literature.
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Sisinni, E., Saifullah, A., Han,S., Jennehag,U.and Gidlund,M., 2018. Industrial internet of things: Challenges, opportunities, and directions. IEEE transactions on industrial informatics, 14(11), pp.4724-4734.
Malik, P.K., Sharma, R., Singh, R., Gehlot,A., Satapathy, S.C.,Alnumay, W.S., Pelusi, D., Ghosh, U. and Nayak, J., 2021. Industrial Internet of Things and its applications in industry 4.0: State of the art. Computer Communications, 166, pp.125-139.
Wan,J., Tang,S., Shu,Z., Li,D., Wang, S.,Imran,M. and Vasilakos,A.V.,2016.Software-defined industrial internet of things in the context of industry 4.0. IEEE Sensors Journal, 16(20), pp.7373-7380.
Bahga,A. and Madisetti,V.K.,2016.Block chain platform for industrial internet of things. Journal of Software Engineering and Applications, 9(10), pp.533-546.
Lu, J., Liu,A., Song,Y. and Zhang, G., 2020. Data-driven decision support under concept drift in streamed big data. Complex & Intelligent Systems, 6(1), pp.157-163.
Seraj, R. and Ahmed, M., 2020. Concept Drift for Big Data. In Combating Security Challenges in the Age of Big Data (pp. 29-43). Springer, Cham.
Sun,H.,He,Q.,Liao,K.,Sellis,T.,Guo,L.,Zhang,X.,Shen,J.andChen,F.,2019,December.Fastanomaly detection in multiple multi-dimensional data streams. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 1218-1223). IEEE.
R oy,A., 2015. A classification algorithm for high-dimensional data. Procedia Computer Science, 53, pp.345-355.
Subbiah,S.S. and Chinnappan,J.,2021. Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review. Ingénierie des Systèmesd' Information, 26(1).
S.Lloyd, “Least squares quantization in PCM,” IEEE Trans .Inf .Theory, vol.IF-28, no.2, pp.129–137,Mar. 1982.
Henning, S. and Hasselbring, W., 2019, December. Scalable and reliable multi-dimensional aggregation of sensor data streams. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 3512-3517). IEEE.
P. S. Bradley, O. L. Mangasarian, and W. N. Street, “Clustering via concave minimization,” in Proc. Adv. Neural Inf. Process. Syst., Denver, CO, USA, 1997, pp. 368–374.
L. Xu and D. Schuurmans, “Unsupervised and semi-supervised multi-class support vector machines,” in Proc. 20th Nat. Conf.Artif. Intell., Pittsburgh, PA, USA, 2005, pp. 904–910.
D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” in Proc. 18th Annu. ACM-SIAM Symp. Discrete Algorithms Soc. Ind. Appl. Math., New Orleans, LA, USA, 2007, pp. 1027–1035.
S.Guha, N.Mishra, R.Motwani, and L.O’Callaghan, “Clustering data streams,” in Proc.41stAnnu.Symp. Found. Comput. Sci., Redondo Beach, CA, USA, 2000, pp. 359–366.
C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, “A framework for clustering evolving data streams,” in Proc. 29th Int. Conf. Very Large Data Bases, Berlin, Germany, 2003, pp. 81–92.
M. R. Ackermann et al., “StreamKM++: A clustering algorithm for data streams,” J. Exp. Algorithmics, vol. 17, no. 1, pp. 173–187, 2012.
M.M.-T.Chiang and B. Mirkin, “Intelligent choice of the number of clusters in k-means clustering: An experimental study with different cluster spreads,” J. Classif., vol. 27, no. 1, pp. 3–40, 2010.
J.A. Hartigan, Clustering Algorithms. New York, NY, USA: Wiley, 1975.
Sun, H., He, Q., Liao, K., Sellis, T., Guo, L., Zhang, X., Shen, J. and Chen,F.,2019, December. Fast anomaly detection in multiple multi-dimensional data streams. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 1218-1223). IEEE.
Borade, J. L. ., & Muddana, A. . (2023). Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 175–191. https://doi.org/10.17762/ijritcc.v11i4.6400
Kamau, J., Goldberg, R., Oliveira, A., Seo-joon, C., & Nakamura, E. Improving Recommendation Systems with Collaborative Filtering Algorithms. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/134
Chang Lee, Deep Learning for Speech Recognition in Intelligent Assistants , Machine Learning Applications Conference Proceedings, Vol 1 2021.
Kumar, A., Dhabliya, D., Agarwal, P., Aneja, N., Dadheech, P., Jamal, S. S., & Antwi, O. A. (2022). Cyber-internet security framework to conquer energy-related attacks on the internet of things with machine learning techniques. Computational Intelligence and Neuroscience, 2022 doi:10.1155/2022/8803586
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