Adaptive multi-protocol communication for energy systems
Article Sidebar
Main Article Content
Abstract
This paper examines approaches to implementing adaptive multi-protocol communication in energy systems that are transforming under the conditions of distributed generation growth and Smart Grid concept development. An architecture is proposed that combines a unified OpenID Connect (OIDC) authentication provider with a machine learning module for dynamic selection of optimal data transmission protocols among MQTT, CoAP, HTTPS, and legacy systems. The solution is based on using an ensemble of algorithms (Random Forest, neural networks, logistic regression) for real-time communication efficiency prediction. The system provides flexible, secure, and scalable management of heterogeneous devices through a single control point. The obtained results demonstrate the potential for reducing communication overhead costs, improving reliability, and creating a foundation for implementing intelligent communication systems in energy sector with automatic protocol switching depending on context and load.
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
1. International Energy Agency. (2023). World Energy Outlook 2023. IEA Publications. Available at: https://www.iea.org/reports/world-energy-outlook-2023
2. IoT Analytics. (2024). State of IoT Summer 2024. Available at: https://iot-analytics.com/number-connected-iot-devices/
3. Ahmad, T., Chen, H., Guo, Y., & Wang, J. (2018). A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand. Energy and Buildings, 165, 301-320. https://doi.org/10.1016/j.enbuild.2018.01.017
4. Zhang, Y., Wang, J., & Wang, X. (2020). Review on probabilistic forecasting of wind power generation with machine learning. Renewable and Sustainable Energy Reviews, 125, 109827. https://doi.org/10.1016/j.rser.2020.109827
5. Dileep, G. (2020). A survey on smart grid technologies and applications. Renewable Energy, 146, 2589–2625. https://doi.org/10.1016/j.renene.2019.08.092
6. Kabalci, Y. (2016). A survey on smart metering and smart grid communication. Renewable and Sustainable Energy Reviews, 57, 302–318. https://doi.org/10.1016/j.rser.2015.12.114
7. Chen, X., McElroy, M. B., Wu, Q., Shu, Y., & Xue, Y. (2019). Transition towards higher penetration of renewables: an overview of interlinked technical, environmental and socio-economic challenges. Journal of Modern Power Systems and Clean Energy, 7(1), 1-18. https://doi.org/10.1007/s40565-018-0438-9
8. Starchenko, V. (2021). Traffic optimization in wifi networks for the internet of things. Scientific Journal of the Ternopil National Technical University, 104(4), 131–142. https://doi.org/10.33108/visnyk_tntu2021.04.131
9. Naik, N. (2017). Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP. In 2017 IEEE International Systems Engineering Symposium (ISSE) (pp. 1–7). IEEE. https://doi.org/10.1109/syseng.2017.8088251
10. Al-Masri, E., et al. (2020). Investigating messaging protocols for the Internet of Things (IoT). IEEE Access, 8, 94880-94911. https://doi.org/10.1109/ACCESS.2020.2993363
11. Thangavel, D., Ma, X., Valera, A., Tan, H.-X., & Tan, C. K.-Y. (2014). Performance evaluation of MQTT and CoAP via a common middleware. In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE. https://doi.org/10.1109/issnip.2014.6827678
12. Rescorla, E. (2018). The Transport Layer Security (TLS) Protocol Version 1.3. RFC 8446. Internet Engineering Task Force. https://doi.org/10.17487/RFC8446
13. Kumar, S., & Patel, D. R. (2019). A survey on Internet of Things: Security and privacy issues. International Journal of Computer Applications, 90(11), 20-26. https://doi.org/10.5120/15764-4454
14. Hardt, D. (2020). The OAuth 2.0 Authorization Framework. RFC 6749 (Updated). Internet Engineering Task Force. https://doi.org/10.17487/RFC6749
15. Jones, M., et al. (2019). OpenID Connect Core 1.0 incorporating errata set 1. OpenID Foundation. https://openid.net/specs/openid-connect-core-1_0.html
16. Martsenyuk, V., & Kit, N. (2024). A multivariate method of forecasting the nonlinear dynamics of production network based on multilayer neural models. Scientific Journal of the Ternopil National Technical University, 114(2), 39–50. https://doi.org/10.33108/visnyk_tntu2024.02.039
17. Combs, G., et al. (2023). Wireshark User's Guide for Wireshark 4.0. Wireshark Foundation. https://www.wireshark.org/docs/wsug_html/
18. Raschka, S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning (Version 3). arXiv. https://doi.org/10.48550/ARXIV.1811.12808
19. International Electrotechnical Commission. (2018). IEC 61850-5-2018: Communication requirements for functions and device models. IEC Publications.
20. IEEE Standards Association. (2018). IEEE Standard 1613-2018: Environmental and Testing Requirements for Communications Networking Devices in Electric Power Substations. IEEE.
21. A. Voloshchuk, D. Velychko, H. Osukhivska, A. Palamar, (2024). Computer system for energy distribution in conditions of electricity shortage using artificial intelligence, in: Proceedings of the 2nd International Workshop on Computer Information Technologies in Industry 4.0 (CITI 2024), Volume 3742, Ternopil, Ukraine, June 12-14, pp. 66–75. Available at: https://ceur-ws.org/Vol-3742/paper5.pdf