Optimal method to monitor network for IoT devices based on anomaly detection

Abstract

Many challenges have been identified to monitor, manage, process, and store the big data that accumulates from different sources in the IoT concept. The focus of this paper is very significant and limited to solving the problem of monitoring classified big data. Detection of anomalies in a grouping of classified data made it easy to monitor and help to make decisions for action to operate. There is no need to store, process, or manage the redundant data further that is already within the range of the group. So, the main concern is abnormal values in the groups that need to be processed further and require focus. The method proposed in this paper serves as an optimal solution designed to address the visualization challenges associated with dense and high-volume datasets. Our approach involves a strategic process of categorizing data into groups and pinpointing anomalies within these groups. This systematic classification not only enhances data organization but also plays a pivotal role in simplifying the visualization of intricate data patterns. Additionally, this method brings about significant cost efficiencies by strategically optimizing the expenses incurred in processing operations and the allocation of storage space for the equipment.

Keywords

• Anomaly detection
• Big Data
• Data monitoring
• IoT

References

1. [1] Sinaeepourfard, A., Hierarchical distributed fog-to-cloud data management in smart cities (Doctoral dissertation, Universitat Politècnica de Catalunya (UPC)). 2017; DOI: 10.5821/dissertation-2117-114435.
2. [2] Sarkar, S., Chatterjee, S., & Misra, S., Assessment of the suitability of fog computing in the context of internet of things. IEEE Transactions on Cloud Computing. 2015; 6(1); 46-59. DOI: 10.1109/TCC.2015.2485206.
3. [3] Bonomi, F., Milito, R., Zhu, J., Addepalli, S., Fog computing and its role in the Internet of things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing. 2012; 13-16. DOI: 10.1145/2342509.2342513.
4. [4] Khan, R., Khan, SU., Zaheer, R., Khan, S., Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges. 10th International Conference on Frontiers of Information Technology. 2012; 257-260. DOI: 10.1109/FIT.2012.53.
5. [5] Yi, S., Hao, Z., Qin, Z., & Li, Q., Fog computing: Platform and applications, Third IEEE Workshop on Hot Topics In Web Systems And Technologies (HotWeb). 2015; 73-78. DOI: 10.1109/HotWeb.2015.22.
6. [6] Munir, A., Kansakar, P., & Khan, S, U., IFCIoT: Integrated Fog Cloud IoT: A novel architectural paradigm for the future Internet of Things. IEEE Consumer Electronics Magazine. 2017; 6(3); 74-82. DOI: 10.1109/MCE.2017.2684981.
7. [7] Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U., The rise of “big data” on cloud computing: Review and open research issues. Information systems. 2015; 98-115. DOI: 10.1016/j.is.2014.07.006
8. [8] Pandey, K. K., Challenges of big data to big data mining with their processing framework, 8th International Conference on Communication Systems and Network Technologies, 2018; 89-94. DOI: 10.1109/CSNT.2018.8820282.

9. [9] Uddin, M. F., & Gupta, N., Seven V's of Big Data understanding Big Data to extract value. In Proceedings of the 2014 zone 1st conference of the American Society for Engineering Education. 2014; 1-5. DOI: 10.1109/ASEEZone1.2014.6820689.
10. [10] Samuel, S. J., Rvp, K., Sashidhar, K., & Bharathi, C. R., A survey on big data and its research challenges. ARPN Journal of Engineering and Applied Sciences. 2015; 10(8); 3343-3347.
11. [11] Chen, C. P., Zhang, C. Y., Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences. 2014; 314-347. DOI: 10.1016/j.ins.2014.01.015.
12. [12] Rossi, R., Hirama, K., Characterizing big data management. Issues in Informing Science and Information Technology. 2015. DOI: 10.48550/arXiv.2201.05929.
13. [13] Demchenko, Y., Ngo, C., de Laat, C., Membrey, P. and Gordijenko, D., Big security for big data: Addressing security challenges for the big data infrastructure. In Secure Data Management: 10th VLDB Workshop, SDM 2013, Trento, Italy, Proceedings 10. 2014; 76-94. DOI: 10.1007/978-3-319-06811-4_13.
14. [14] Narasimhan, R., & Bhuvaneshwari, T, Big data-a brief study. Int. J. Sci. Eng. 2014; 5(9); 350-353.
15. [15] Eileen, M., DATACONOMY. Understanding big data: the seven V's [Internet]. Accessed June 2022. Available from: http://dataconomy.com/seven-vs-big-data/.
16. [16] Wang, L., Wang, G., & Alexander, C. A., Big data and visualization: methods, challenges and technology progress. Digital Technologies. 2015; 1(1); 33-38.
17. [17] Mineraud, J., Mazhelis, O., Su, X., & Tarkoma, S., A gap analysis of Internet-of-Things platforms. Computer Communications. 2016; 8; 5-16. DOI: 10.1016/j.comcom.2016.03.015.
18. [18] Perera, C., Liu, C. H., Jayawardena, S., & Chen, M., A survey on internet of things from industrial market perspective. IEEE Access, 2014; 1660-1679. DOI: 10.1109/ACCESS.2015.2389854.
19. [19] Perera, C., Liu, C. H., & Jayawardena, S., The emerging internet of things marketplace from an industrial perspective: A survey. IEEE Transactions on Emerging Topics in Computing, 2015; 3(4); 585-598. DOI: 10.1109/TETC.2015.2390034.
20. [20] Dorri, A., Kanhere, S. S., Jurdak, R., & Gauravaram, P., Blockchain for IoT security and privacy: The case study of a smart home. In 2017 IEEE international conference on pervasive computing and communications workshops (PerCom workshops). 2017; 618-623. DOI: 10.1109/PERCOMW.2017.7917634.
21. [21] Kakani, P. N., & Rajendran, L., Flexible Communication Technologies Utilized in Developing Smart Cities. In Smart Cities. 2022; 245-267.
22. [22] Minoli, D., Sohraby, K., & Occhiogrosso, B., IoT considerations, requirements, and architectures for smart buildings—Energy optimization and next-generation building management systems. IEEE Internet of Things Journal. 2017; 4(1); 269-283. DOI: 10.1109/JIOT.2017.2647881.
23. [23] Vermesan, O., & Friess, P. (Eds.)., Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River publishers. 2013.
24. [24] Han, D. M., & Lim, J. H., Smart home energy management system using IEEE 802.15. 4 and zigbee. IEEE Transactions on Consumer Electronics. 2010; 56(3); 1403-1410. DOI: 10.1109/TCE.2010.5606276.
25. [25] Chen, C., Duan, S., Cai, T., Liu, B., & Hu, G., Smart energy management system for optimal microgrid economic operation. IET Renewable Power Generation. 2011; 5(3); 258-267. DOI: 10.1049/iet-rpg.2010.0052.
26. [26] Zhu, Z. T., Yu, M. H., & Riezebos, P., A research framework of smart education. Smart Learning Environments. 2016; 3; 1-17. DOI: 10.1186/s40561-016-0026-2
27. [27] Jeong, J. S., Kim, M., & Yoo, K. H., A content oriented smart education system based on cloud computing. International Journal of Multimedia and Ubiquitous Engineering. 2013; 8(6); 313-328. DOI: 10.14257/ijmue.2013.8.6.31
28. [28] Tikhomirov, V., Dneprovskaya, N., & Yankovskaya, E., Three dimensions of smart education. In Smart Education and Smart e-Learning. Springer International Publishing. 2015; 47-56. DOI: 10.1007/978-3-319-19875-0_5.
29. [29] Lehmann, E. E., Seitz, N., & Wirsching, K., Smart finance for smart places to foster new venture creation. Economia e Politica Industriale. 2015; 44; 51-75. DOI: 10.1007/s40812-016-0052-7.
30. [30] Catarinucci, L., De Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., & Tarricone, L., An IoT-aware architecture for smart healthcare systems. IEEE Internet Of Things Journal. 2015; 2(6); 515-526. DOI: DOI: 10.1109/JIOT.2015.2417684.
31. [31] Al Mamun, S. A., & Valimaki, J., Anomaly detection and classification in cellular networks using automatic labeling technique for applying supervised learning. Procedia Computer Science. 2018; 140; 186-195. DOI: 10.1016/j.procs.2018.10.328.
32. [32] Wang, J., Tang, Y., Nguyen, M., & Altintas, I., A scalable data science workflow approach for big data bayesian network learning. In 2014 IEEE/ACM International Symposium on Big Data Computing. 2014; 16-25. DOI: 10.1109/BDC.2014.10
33. [33] Kolias, V., Anagnostopoulos, I., & Kayafas, E., A Covering Classification Rule Induction Approach for Big Datasets. In 2014 IEEE/ACM International Symposium on Big Data Computing. 2014; 45-53. DOI: 10.1109/BDC.2014.17.
34. [34] Mark, A., The Spatial Internet of Things. [Internet]. Accessed June 2022. Available from: https://www.gislounge.com/the-spatial-internet-of-things/
35. [35] Arridha, R., Sukaridhoto, S., Pramadihanto, D., & Funabiki, N., Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system. International Journal of Space-Based and Situated Computing. 2017; 7(2); 82-93. DOI: 10.1504/IJSSC.2017.086821.