ONTOLOGICAL APPROACH TO LOAD DISTRIBUTION OF THE INTERNET OF THINGS
DOI:
https://doi.org/10.26906/SUNZ.2024.2.111Keywords:
Internet of Things, fog computing, cloud system, ontology, production rulesAbstract
Topicality. Today, Internet of Things systems will gain more and more popularity in various industries. With a large number of finite sensors, the concept of fog computing is actively used. But the use of the fuzzy concept in such cases requires quite frequent redistribution of the load between computing nodes. The purpose of the article is to develop an approach to reducing time spent on load redistribution by reducing the number of candidate nodes for load placement and the time of its formation based on ontological analysis in order to increase the efficiency of the functioning of a distributed system implemented on the basis of fog computing technology. Research results. An analysis of the use of ontologies for solving optimization tasks was carried out. The proposed step-by-step method of forming the computing load distribution ontology. The approach for forming a system of production rules for selecting nodes for transferring the load of the Internet of Things is described. An example of the application of the developed approach in the implementation of evolutionary algorithms used to analyze data received from the sensors of the Internet of Things system is given. Conclusion. The developed approach reduces time spent on load redistribution by reducing the number of candidate nodes for load placement and the time of its formation based on ontological analysis with the use of fog computing technology.Downloads
References
Pardo, C., Wei, R., Ivens, B.S. (2022). Integrating the business networks and internet of things perspectives: A system of systems (SoS) approach for industrial markets. Industrial Marketing Management, 104, 258–275. doi: https://doi.org/10.1016/j.indmarman.2022.04.012
Zakharchenko, A., Stepanets, O. (2023). Digital twin value in intelligent building development. Advanced Information Systems, 7(2), 75–86. doi: https://doi.org/10.20998/2522-9052.2023.2.11
Li, G., Liu, Y., Wu, J., Lin, D., Zhao, Sh. (2019). Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing. Sensors, MDPI, 19(9). doi: https://doi.org/10.3390/s19092122
Schulz, A.S. (2023). User Interactions with Internet of Things (IoT) Devices in Shared Domestic Spaces. ACM International Conference Proceeding Series, 577–579. doi: https://doi.org/10.1145/3626705.3632615
Chalapathi, G.S.S., Chamola, V., Vaish, A., Buyya, R. (2022). Industrial internet of things (Iiot) applications of edge and fog computing: A review and future directions. Advances in Information Security, 83, 293–325. doi: https://doi.org/10.1007/978-3-030-57328-7_12
Petrovska, I., Kuchuk, H. (2023). Adaptive resource allocation method for data processing and security in cloud environment. Advanced Information Systems, 7(3), 67–73. doi: https://doi.org/10.20998/2522-9052.2023.3.10
Qayyum, T., Trabelsi, Z., Waqar Malik, A., Hayawi, K. (2022). Mobility-aware hierarchical fog computing framework for Industrial Internet of Things. Journal of Cloud Computing, 11(1), 72. doi: https://doi.org/10.1186/s13677-022-00345-y
Mishra, A., Singh, P. (2024), A hybrid approach to ontology evaluation. Mathematics and Computer Science, vol. 2, рр. 187–204, doi: DOI: https://doi.org/10.1002/9781119896715.ch13
Kuchuk, G., Nechausov, S., Kharchenko, V. (2015), Two-stage optimization of resource allocation for hybrid cloud data store. International Conference on Information and Digital Technologies, Zilina, pp. 266–271. DOI: http://dx.doi.org/10.1109/DT.2015.7222982
Zuev, A., Karaman, D., Olshevskiy, A. (2023). Wireless sensor synchronization method for monitoring short-term events. Advanced Information Systems, 7(4), 33–40. doi: https://doi.org/10.20998/2522-9052.2023.4.04
Kuchuk G., Kovalenko A., Komari I.E., Svyrydov A., Kharchenko V. Improving big data centers energy efficiency: Traffic based model and method. Studies in Systems, Decision and Control, vol 171. Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (Eds.). Springer Nature Switzerland AG, 2019. Pp. 161-183. DOI: http://doi.org/10.1007/978-3-030-00253-4_8
Коваленко А. А., Кучук Г. А. Методи синтезу інформаційної та технічної структур системи управління об’єктом критичного застосування. Сучасні інформаційні системи. 2018. Т. 2, № 1. С. 22–27. DOI: https://doi.org/10.20998/2522-9052.2018.1.04
Nechausov A., Mamusuĉ I., Kuchuk N. Synthesis of the air pollution level control system on the basis of hyperconvergent infrastructures. Сучасні інформаційні системи. 2017. Т. 1, № 2. С. 21 – 26. DOI: https://doi.org/10.20998/2522-9052.2017.2.04
Кучук Н. Г., Мерлак В. Ю., Скородєлов В. В. Метод зменшення часу доступу до слабкоструктурованих даних. Сучасні інформаційні системи. 2020. Т. 4, № 1. С. 97-102. doi: https://doi.org/10.20998/2522-9052.2020.1.14
Qaswar, F., Rahmah, M., Raza, M.A., Hassan, M.K.A., Sharaf, A. (2023), Applications of Ontology in the Internet of Things: A Systematic Analysis, Electronics (Switzerland), 12(1), 111, doi: http://dx.doi.org/10.3390/electronics12010111
Kalaiselvi, P., Michael Jones, M., Murugesh, S., Veerakumar, K., Prakash, N. (2023), Design And Implementation of Smart Billing System, 2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023, страницы 212–218, doi: http://dx.doi.org/10.1109/ICSEIET58677.2023.10303608
Jayasuriya, D.B., Perera, I. (2019), Ontology Based Software Design Documentation for Design Reasoning, MERCon 2019 - Proceedings, 5th International Multidisciplinary Moratuwa Engineering Research Conference, pp. 710–715, 8818813, doi: http://dx.doi.org/10.1109/MERCon.2019.8818813
Lee, C.-H.L., Liu, A. (2008), Applying fuzzy candlestick pattern ontology to investment knowledge management, Journal of Internet Technology, 9(4), pp. 307–315.
