AI-driven graph analysis of IoT networks using discrete structures

Chae, Gyoo Soo

doi:10.47974/JDMSC-2515

Open Access Research Article

AI-driven graph analysis of IoT networks using discrete structures

^*Gyoo Soo ChaeCorresponding authorgschae@bu.ac.krDivision of Advanced Information TechnologyBaekseok UniversityCheonan City, 31065, Republic of KoreaView full profile →

* Corresponding author · click or hover a name for details

pp. 701–709Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2515 Crossmark XML

Received:: 16 May 2025
Published Online:: 04 Feb 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2515
Pages:: 701–709

Abstract

As Internet of Things (IoT) networks grow quickly, it gets harder to look at data and make networks work better. We study how discrete mathematics structures, like graph theory, partly ordered sets (posets), and lattices, can be used together to model how IoT networks are set up in this paper. This study uses two advanced AI methodologies, Graph Neural Networks (GNNs) and reinforcement learning, to come up with new ways to look at and make judgements about complicated network behaviours. By combining discrete structures with AI-driven methodologies, this strategy makes it easier to recognize patterns, find problems, and share resources in IoT environments.

Keywords

Internet of things Graph neural networks Discrete mathematics Network topology Reinforcement learning Anomaly detection

Subject Classifications

31A1033E30

References

[1] N. Cheng, S. Wu, X. Wang, Z. Yin, C. Li, W. Chen, and F. Chen, “AI for UAV-assisted IoT applications: A comprehensive review,” IEEE Internet Things J., vol. 10, pp. 14438–14461 (2023).
[2] Y. Ma, K. Ping, C. Wu, L. Chen, H. Shi, and D. Chong, “Artificial intelligence powered Internet of Things and smart public service,” Library Hi Tech, vol. 38, pp. 165–179 (2020).
[3] I. Kök, F. Y. Okay, Ö. Muyanlı, and S. Özdemir, “Explainable artificial intelligence (XAI) for Internet of Things: A survey,” IEEE Internet Things J., vol. 10, pp. 14764–14779 (2023).
[4] D. Mohan, D. Z. Al-Hamid, P. H. J. Chong, K. L. K. Sudheera, J. Gutierrez, H. C. Chan, and H. Li, “Artificial intelligence and IoT in elderly fall prevention: A review,” IEEE Sens. J., vol. 24, pp. 4181–4198 (2024).
[5] J. Pwavodi, A. U. Ibrahim, P. C. Pwavodi, F. Al-Turjman, and A. Mohand-Said, “The role of artificial intelligence and IoT in prediction of earthquakes,” Artif. Intell. Geosci., vol. 5, pp. 100075 (2024).
[6] U. F. Mustapha, A. W. Alhassan, D. N. Jiang, and G. L. Li, “Sustainable aquaculture development: A review on the roles of cloud computing, Internet of Things and artificial intelligence (CIA),” Rev. Aquacult., vol. 13, pp. 2076–2091 (2021).
[7] N. Misra, Y. Dixit, A. Al-Mallahi, M. S. Bhullar, R. Upadhyay, and A. Martynenko, “IoT, big data, and artificial intelligence in agriculture and food industry,” IEEE Internet Things J., vol. 9, pp. 6305–6324 (2020).
[8] S. H. Kulkarni, V. V. Deshmukh, R. K. Moje, and S. H. Lavate, “Applied discrete mathematics in developing efficient cryptographic algorithms for enhancing information security in WSN,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1733–142 (2025).
[9] Y. Liang, S. Samtani, B. Guo, and Z. Yu, “Behavioral biometrics for continuous authentication in the Internet-of-Things era: An artificial intelligence perspective,” IEEE Internet Things J., vol. 7, pp. 9128–9143 (2020).
[10] A. K. Mohapatra, P. Lathar, and S. S. Gaur, “A novel security algorithm for text and image data using multi node cryptographic algorithm,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 3, pp. 753–764 (2025).
[11] H. Orchi, M. Sadik, and M. Khaldoun, “On using artificial intelligence and the Internet of Things for crop disease detection: A contemporary survey,” Agriculture, vol. 12, pp. 9 (2021).
[12] A. Khanpurkar, P. K. Tembhekar, J. N. Bagde, R. D. Janbandhu, K. R. Moharkar, and S. W. Godhane, “Design and development of an Arduino-based intelligent object sorting system,” Int. J. Technol. Adv. Res. Mech. Eng. (IJTARME), vol. 14, no. 1, pp. 29–35 (May 2025).
[13] C. Greco, G. Fortino, B. Crispo, and K. K. R. Choo, “AI-enabled IoT penetration testing: State-of-the-art and research challenges,” Enterprise Inf. Syst., vol. 17, pp. 2130014 (2023).

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