Anomaly Detection and Performance Analysis by Using Big Data Filtering Techniques For Healthcare on IoT Edges

Year 2022, , 1 - 13, 28.02.2022

Şükrü Mustafa Kaya , Atakan Erdem , Ali Güneş

https://doi.org/10.16984/saufenbilder.903915

Cited By: 4

Abstract

The IoT is a sensors world that detects countless physical events in our environment and transforms them into data, and transfers this data to different environments or digital systems. The usage areas of Internet of things-based technologies are constantly increasing and technologies are being developed to support the IoT infrastructure. But, in order to effectively manage the large number of big-data generate in the detection layer, it should be pre-processed and done in accordance with big-data standards. For the effective management of big data, it is imperative to improving the standards of the data set, and filtering methods are being developed for a higher quality data set. For instance, using data cleaning methods is a preprocessing method that facilitates data mining operations. In this way, more manageable data is obtained by preventing the formation of interference and big data can be managed more effectively. In this study, we investigate the efficient operation of IoT and big data originating from the internet of things. Additionally, real-time anomalous data filtering is performed on IoT edges with a data set consisting of six different data produced in real- time. Furthermore, the speed and accuracy performances of classifiers are compared, and machine learning algorithms such as the random cut forest-RCF, logistic regression-LR, naive bayes-NB, and neural network-NN classifiers are used for comparison. According to the accuracy performance values, the RCF and LR classifiers are very close, but considering the speed values, it is seen that the LR classifier is more successful in IoT systems.

Keywords

Internet of Things, Big Data Management, Big Data Analytics, data preprocessing

References

• [1] T. Taneja, A. Jatain, S.B. Bajaj., “Predictive Analytics on IoT,” International Conference on Computing, Communication and Automation., 2017.
• [2] M. Ahmed, S. Choudhury, “Big Data Analytics for Internet of Things,” https://www.researchgate.net/publication/323163119, 2018.
• [3] D.P. Acharjya, A.P. Kauser, “A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 2, pp. 511-5187, 2016.
• [4] P. Gupta, R. Gupra, “Data Mining Framework for IoT Applications,” International Journal of Computer Applications (0975 – 8887), vol. 174, no. 2, pp. 4-7, 2017.
• [5] H. Yar, A.S. Imran, Z.A. Khan, M. Sajjad, Z. Kastrati, “Towards Smart Home Automation Using IoT-Enabled Edge-Computing Paradigm,” Sensors, vol. 21, no. 4, 4932, 2021.
• [6] S. Hamdan, M. Ayyash, S. Almajali, “Edge-Computing Architectures for Internet of Things Applications: A Survey,” Sensors, 20, 6441, 2020.
• [7] M. Peyman, P.J. Copado, R.D. Tordecilla, L.C. Martins, F. Xhafa, A.A. Juan, “Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems,” Energies, 14, 6309, 2021.
• [8] A.H. Tasin, Ummasalma, Likhonbarua, Md. S. Hossain, S. Datta, A. Pathak, “IoT Based Low-Cost System For Monitoring Water Quality Of Karnaphuli River To Save The Ecosystem In Real-Time Environment,” American Journal of Engineering Research (AJER), vol. 9, no. 2, pp-60-72, 2020.
• [9] H. Aly, M. Elmogy, S. Barakat, “Big Data on Internet of Things: Applications, Architecture, Technologies, Techniques, and Future Directions,” International Journal of Computer Science Engineering (IJCSE), ISSN: 2319-7323, vol. 4, pp. 300-313, 2015.
• [10] P. Gulia, A. Chahal, Big Data Analytics For IoT, International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 11, no. 6, pp. 593-603, 2020.
• [11] N. Yadav, Er. P. Verma, Er. S. Srivastava, “Role of IoT in Big Data,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 8, no. XII, pp. 516-522, 2020.
• [12] B. Nemane, R.D. Pahurkar, “Security Challenges in IOT, Big Data & Cloud Computing Integration,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 9, no. II, 2021.
• [13] R.S.B. Cokro, E.Y. Wirawan, Y. Putra, A. Puspitarini, G. Wang, E.R. Kaburuan, “Designing Smart Parking System through the Use of IoT and Big Data,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 10, no. 5, 2021.
• [14] V.S.S.J. Kodidala, S. Akkala, S.K. Mdupoju, V.S.S.T. Dasara, M. Juvvadi, N. Thangadurani, “Big Data analysis of demand side management for Industrial IoT applications,” Materials Today: Proceedings, Elsevier, 2021.
• [15] B.B.P. Sushree, B. Amiya, K.M. Brojo, “The Role of IoT and Big Data in Modern Technological Arena: A Comprehensive Study,” Intelligent Systems Reference Library, vol. 154, pp. 13-25, 2019.
• [16] R. Ranjan, D. Thakker, A. Haller, R. Buyya, “A note on the exploration of IoT generated big data using semantics,” Future Generation Computer Systems., vol. 76, pp. 495-498, 2017.
• [17] X. Li, H.N. Dai, Q. Wang, M. Imran, D. Li, M.A. Imran, “Securing Internet of Medical Things with Friendly-jamming schemes, Computer Communications,” vol. 160, pp. 431–442, 2020.
• [18] P.Y. Sai, P. Harika, “Illustration of IoT with Big Data Analytics,” Global Journal of Computer Science and Technology, vol. XVII, no. III, Version I., 2017.
• [19] Ş.M. Kaya, A. Erdem, A. Güneş, “A Smart Data Pre-Processing Approach to Effective Management of Big Health Data in IoT Edge,” Smart Homecare Technology and TeleHealth, no. 8, pp. 9-21, 2021.
• [20] E. Ahmed, I. Yaqoop, I.A.T. Hashem, I. Khan, A.I.A. Ahmed, M. Imran, A.V. Vasilakos, “The Role Of Big Data Analytics In Internet Of Things,” Computer Networks, vol. 129, no. 2, pp. 459-471, 2017.
• [21] J. Saldatos, “Building Blocks for IoT Analytics Internet-of-Things Analytics,” Published, sold and distributed by River Publishers, Alsbjergvej 10, 9260 Gistrup, Denmark, 2017.
• [22] M. Ge, H. Bangui, B. Buhnova, “Big Data for the Internet of Things: A Survey,” Future Generation Computer Systems, vol. 87, pp. 601-614, 2018.
• [23] E. Ahmed, M.H. Rehmani, “Mobile Edge Computing: Opportunities, Solutions, and Challenges,” Future Generation Computer Systems, vol. 70, pp. 59-63, 2016.
• [24] Ş.M. Kaya, A. Güneş, A. Erdem, “A Smart Data Pre-Processing Approach by Using ML Algorithms on IoT Edges: A Case Study.” 2021 International Conference on Artificial Intelligence of Things (ICAIoT) (pp. 36-42). IEEE, 2021.
• [25] P. Wlodarczak, M. Ally, J. Soar, “Data Mining in IoT,” Association for Computing Machinery. ACM ISBN 978-1-4503-4951, 2017.
• [26] Ş.M. Kaya,, “A smart data pre-processing approach for effective management of healthcare big data on IoT edges,” Istanbul Aydın University, Graduate School of Natural and Applied Sciences, Department of Computer Engineering, PhD Thesis., 2021.
• [27] F. Chen, P. Deng, J. Wan, D. Zhang, A.V. Vasilakos, X. Rong, “Data Mining for the Internet of Things: Literature Review and Challenges,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 431047, 14 pages, 2015.
• [28] S. Naveen, S.G. Hegde, “Study of IoT: Understanding IoT Architecture, Applications, Issues and Challenges,” International Journal of Advanced Networking & Applications (IJANA), ISSN: 0975-0282., pp. 477-482, 2019.
• [29] K. Sha, T.A. Yang, W. Wei, S. Davari, “A survey of edge computing-based designs for IoT security,” Digital Communications and Networks, vol. 6, no.2, pp. 195-202, 2019.
• [30] D.Y. Kim, Y.S. Jeong, S. Kim, “Data-Filtering System to Avoid Total Data Distortion in IoT Networking,” Symmetry vol. 9, no, 16, 2017.